Overview

Brought to you by YData

Dataset statistics

Number of variables57
Number of observations20042
Missing cells257523
Missing cells (%)22.5%
Duplicate rows28
Duplicate rows (%)0.1%
Total size in memory49.1 MiB
Average record size in memory2.5 KiB

Variable types

Categorical14
Text17
Numeric18
DateTime1
Unsupported7

Alerts

Dataset has 28 (0.1%) duplicate rowsDuplicates
A- is highly overall correlated with B+ and 1 other fieldsHigh correlation
Average Grade is highly overall correlated with B and 4 other fieldsHigh correlation
B is highly overall correlated with Average Grade and 1 other fieldsHigh correlation
B+ is highly overall correlated with A- and 2 other fieldsHigh correlation
B- is highly overall correlated with A- and 4 other fieldsHigh correlation
C is highly overall correlated with Average Grade and 2 other fieldsHigh correlation
C+ is highly overall correlated with Average Grade and 3 other fieldsHigh correlation
C- is highly overall correlated with Average Grade and 2 other fieldsHigh correlation
Course is highly overall correlated with NumberHigh correlation
Credit Hours is highly overall correlated with Section Credit HoursHigh correlation
D is highly overall correlated with CHigh correlation
Number is highly overall correlated with CourseHigh correlation
Sched Type is highly overall correlated with TypeHigh correlation
Section Credit Hours is highly overall correlated with Credit HoursHigh correlation
Section Status is highly overall correlated with Status CodeHigh correlation
Status Code is highly overall correlated with Section StatusHigh correlation
Term is highly overall correlated with YearTerm and 1 other fieldsHigh correlation
Type is highly overall correlated with Sched TypeHigh correlation
Year is highly overall correlated with YearTerm and 1 other fieldsHigh correlation
YearTerm is highly overall correlated with Term and 2 other fieldsHigh correlation
source_file is highly overall correlated with Term and 2 other fieldsHigh correlation
Credit Hours is highly imbalanced (54.9%) Imbalance
Status Code is highly imbalanced (99.7%) Imbalance
Part of Term is highly imbalanced (60.6%) Imbalance
Section Status is highly imbalanced (99.7%) Imbalance
Section Info has 4055 (20.2%) missing values Missing
Degree Attributes has 14998 (74.8%) missing values Missing
Schedule Information has 16724 (83.4%) missing values Missing
Section_x has 1061 (5.3%) missing values Missing
Part of Term has 359 (1.8%) missing values Missing
Section Title has 18703 (93.3%) missing values Missing
Section Credit Hours has 15449 (77.1%) missing values Missing
End Time has 2900 (14.5%) missing values Missing
Days of Week has 2855 (14.2%) missing values Missing
Room has 7703 (38.4%) missing values Missing
Building has 7703 (38.4%) missing values Missing
Course has 20042 (100.0%) missing values Missing
Course Subject has 20042 (100.0%) missing values Missing
Course Number has 20042 (100.0%) missing values Missing
Course Section has 6848 (34.2%) missing values Missing
A Range has 20042 (100.0%) missing values Missing
B Range has 20042 (100.0%) missing values Missing
C Range has 20042 (100.0%) missing values Missing
D Range has 20042 (100.0%) missing values Missing
Section_y has 17781 (88.7%) missing values Missing
Course is an unsupported type, check if it needs cleaning or further analysis Unsupported
Course Subject is an unsupported type, check if it needs cleaning or further analysis Unsupported
Course Number is an unsupported type, check if it needs cleaning or further analysis Unsupported
A Range is an unsupported type, check if it needs cleaning or further analysis Unsupported
B Range is an unsupported type, check if it needs cleaning or further analysis Unsupported
C Range is an unsupported type, check if it needs cleaning or further analysis Unsupported
D Range is an unsupported type, check if it needs cleaning or further analysis Unsupported
A+ has 5049 (25.2%) zeros Zeros
A- has 2918 (14.6%) zeros Zeros
B+ has 3856 (19.2%) zeros Zeros
B has 3123 (15.6%) zeros Zeros
B- has 8033 (40.1%) zeros Zeros
C+ has 10484 (52.3%) zeros Zeros
C has 9463 (47.2%) zeros Zeros
C- has 13344 (66.6%) zeros Zeros
D+ has 15896 (79.3%) zeros Zeros
D has 14328 (71.5%) zeros Zeros
D- has 16846 (84.1%) zeros Zeros
F has 11338 (56.6%) zeros Zeros
W has 17728 (88.5%) zeros Zeros

Reproduction

Analysis started2025-05-12 01:40:53.134532
Analysis finished2025-05-12 01:41:42.951523
Duration49.82 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Year
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2022
5191 
2023
5141 
2021
5126 
2020
4584 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters80168
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2022 5191
25.9%
2023 5141
25.7%
2021 5126
25.6%
2020 4584
22.9%

Length

2025-05-12T01:41:43.040004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T01:41:43.111421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2022 5191
25.9%
2023 5141
25.7%
2021 5126
25.6%
2020 4584
22.9%

Most occurring characters

ValueCountFrequency (%)
2 45275
56.5%
0 24626
30.7%
3 5141
 
6.4%
1 5126
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 45275
56.5%
0 24626
30.7%
3 5141
 
6.4%
1 5126
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 45275
56.5%
0 24626
30.7%
3 5141
 
6.4%
1 5126
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 45275
56.5%
0 24626
30.7%
3 5141
 
6.4%
1 5126
 
6.4%

Term
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
FALL
10917 
SPRING
9125 

Length

Max length6
Median length4
Mean length4.9105878
Min length4

Characters and Unicode

Total characters98418
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFALL
2nd rowFALL
3rd rowFALL
4th rowFALL
5th rowFALL

Common Values

ValueCountFrequency (%)
FALL 10917
54.5%
SPRING 9125
45.5%

Length

2025-05-12T01:41:43.215971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T01:41:43.298125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fall 10917
54.5%
spring 9125
45.5%

Most occurring characters

ValueCountFrequency (%)
L 21834
22.2%
F 10917
11.1%
A 10917
11.1%
S 9125
9.3%
P 9125
9.3%
R 9125
9.3%
I 9125
9.3%
N 9125
9.3%
G 9125
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 98418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 21834
22.2%
F 10917
11.1%
A 10917
11.1%
S 9125
9.3%
P 9125
9.3%
R 9125
9.3%
I 9125
9.3%
N 9125
9.3%
G 9125
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 98418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 21834
22.2%
F 10917
11.1%
A 10917
11.1%
S 9125
9.3%
P 9125
9.3%
R 9125
9.3%
I 9125
9.3%
N 9125
9.3%
G 9125
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 98418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 21834
22.2%
F 10917
11.1%
A 10917
11.1%
S 9125
9.3%
P 9125
9.3%
R 9125
9.3%
I 9125
9.3%
N 9125
9.3%
G 9125
9.3%

YearTerm
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2021-fa
2865 
2023-fa
2841 
2022-fa
2807 
2020-fa
2404 
2022-sp
2384 
Other values (3)
6741 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters140294
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-fa
2nd row2020-fa
3rd row2020-fa
4th row2020-fa
5th row2020-fa

Common Values

ValueCountFrequency (%)
2021-fa 2865
14.3%
2023-fa 2841
14.2%
2022-fa 2807
14.0%
2020-fa 2404
12.0%
2022-sp 2384
11.9%
2023-sp 2300
11.5%
2021-sp 2261
11.3%
2020-sp 2180
10.9%

Length

2025-05-12T01:41:43.376920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T01:41:43.473273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2021-fa 2865
14.3%
2023-fa 2841
14.2%
2022-fa 2807
14.0%
2020-fa 2404
12.0%
2022-sp 2384
11.9%
2023-sp 2300
11.5%
2021-sp 2261
11.3%
2020-sp 2180
10.9%

Most occurring characters

ValueCountFrequency (%)
2 45275
32.3%
0 24626
17.6%
- 20042
14.3%
f 10917
 
7.8%
a 10917
 
7.8%
p 9125
 
6.5%
s 9125
 
6.5%
3 5141
 
3.7%
1 5126
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 140294
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 45275
32.3%
0 24626
17.6%
- 20042
14.3%
f 10917
 
7.8%
a 10917
 
7.8%
p 9125
 
6.5%
s 9125
 
6.5%
3 5141
 
3.7%
1 5126
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 140294
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 45275
32.3%
0 24626
17.6%
- 20042
14.3%
f 10917
 
7.8%
a 10917
 
7.8%
p 9125
 
6.5%
s 9125
 
6.5%
3 5141
 
3.7%
1 5126
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 140294
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 45275
32.3%
0 24626
17.6%
- 20042
14.3%
f 10917
 
7.8%
a 10917
 
7.8%
p 9125
 
6.5%
s 9125
 
6.5%
3 5141
 
3.7%
1 5126
 
3.7%
Distinct157
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2025-05-12T01:41:43.816249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.3756112
Min length2

Characters and Unicode

Total characters67654
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.1%

Sample

1st rowAAS
2nd rowAAS
3rd rowAAS
4th rowAAS
5th rowAAS
ValueCountFrequency (%)
econ 931
 
4.6%
badm 923
 
4.6%
accy 787
 
3.9%
mcb 701
 
3.5%
psyc 690
 
3.4%
math 613
 
3.1%
cs 591
 
2.9%
ece 590
 
2.9%
cmn 519
 
2.6%
fin 496
 
2.5%
Other values (147) 13201
65.9%
2025-05-12T01:41:44.252542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 9645
14.3%
S 7533
11.1%
A 6473
 
9.6%
E 6448
 
9.5%
M 4456
 
6.6%
N 3894
 
5.8%
H 3607
 
5.3%
T 3083
 
4.6%
L 2755
 
4.1%
I 2745
 
4.1%
Other values (14) 17015
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 9645
14.3%
S 7533
11.1%
A 6473
 
9.6%
E 6448
 
9.5%
M 4456
 
6.6%
N 3894
 
5.8%
H 3607
 
5.3%
T 3083
 
4.6%
L 2755
 
4.1%
I 2745
 
4.1%
Other values (14) 17015
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 9645
14.3%
S 7533
11.1%
A 6473
 
9.6%
E 6448
 
9.5%
M 4456
 
6.6%
N 3894
 
5.8%
H 3607
 
5.3%
T 3083
 
4.6%
L 2755
 
4.1%
I 2745
 
4.1%
Other values (14) 17015
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 9645
14.3%
S 7533
11.1%
A 6473
 
9.6%
E 6448
 
9.5%
M 4456
 
6.6%
N 3894
 
5.8%
H 3607
 
5.3%
T 3083
 
4.6%
L 2755
 
4.1%
I 2745
 
4.1%
Other values (14) 17015
25.2%

Number
Real number (ℝ)

High correlation 

Distinct508
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.99736
Minimum100
Maximum798
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:41:44.393934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1196
median301
Q3437
95-th percentile571
Maximum798
Range698
Interquartile range (IQR)241

Descriptive statistics

Standard deviation156.10712
Coefficient of variation (CV)0.50684566
Kurtosis-0.99884377
Mean307.99736
Median Absolute Deviation (MAD)131
Skewness0.24380412
Sum6172883
Variance24369.434
MonotonicityNot monotonic
2025-05-12T01:41:44.529534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1211
 
6.0%
101 1185
 
5.9%
102 361
 
1.8%
201 320
 
1.6%
103 285
 
1.4%
150 248
 
1.2%
250 245
 
1.2%
202 221
 
1.1%
199 216
 
1.1%
104 213
 
1.1%
Other values (498) 15537
77.5%
ValueCountFrequency (%)
100 1211
6.0%
101 1185
5.9%
102 361
 
1.8%
103 285
 
1.4%
104 213
 
1.1%
105 78
 
0.4%
106 16
 
0.1%
107 31
 
0.2%
108 21
 
0.1%
109 37
 
0.2%
ValueCountFrequency (%)
798 1
 
< 0.1%
797 6
 
< 0.1%
796 3
 
< 0.1%
795 2
 
< 0.1%
794 8
< 0.1%
792 18
0.1%
694 4
 
< 0.1%
686 3
 
< 0.1%
684 3
 
< 0.1%
682 7
 
< 0.1%

Name
Text

Distinct3021
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
2025-05-12T01:41:44.862024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length99
Median length72
Mean length26.273775
Min length3

Characters and Unicode

Total characters526579
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique670 ?
Unique (%)3.3%

Sample

1st rowIntro Asian American Studies
2nd rowIntro Asian American Studies
3rd rowIntro Asian American Studies
4th rowIntro Asian American Studies
5th rowIntro Asian American Studies
ValueCountFrequency (%)
and 3139
 
4.4%
to 2170
 
3.0%
in 1955
 
2.7%
amp 1855
 
2.6%
of 1757
 
2.5%
intro 1300
 
1.8%
introduction 1225
 
1.7%
i 921
 
1.3%
the 817
 
1.1%
design 753
 
1.1%
Other values (2201) 55403
77.7%
2025-05-12T01:41:45.345072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51253
 
9.7%
i 42161
 
8.0%
n 40905
 
7.8%
e 39051
 
7.4%
o 35232
 
6.7%
a 35040
 
6.7%
t 33441
 
6.4%
r 27552
 
5.2%
s 25316
 
4.8%
c 23218
 
4.4%
Other values (68) 173410
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 526579
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
51253
 
9.7%
i 42161
 
8.0%
n 40905
 
7.8%
e 39051
 
7.4%
o 35232
 
6.7%
a 35040
 
6.7%
t 33441
 
6.4%
r 27552
 
5.2%
s 25316
 
4.8%
c 23218
 
4.4%
Other values (68) 173410
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 526579
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
51253
 
9.7%
i 42161
 
8.0%
n 40905
 
7.8%
e 39051
 
7.4%
o 35232
 
6.7%
a 35040
 
6.7%
t 33441
 
6.4%
r 27552
 
5.2%
s 25316
 
4.8%
c 23218
 
4.4%
Other values (68) 173410
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 526579
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
51253
 
9.7%
i 42161
 
8.0%
n 40905
 
7.8%
e 39051
 
7.4%
o 35232
 
6.7%
a 35040
 
6.7%
t 33441
 
6.4%
r 27552
 
5.2%
s 25316
 
4.8%
c 23218
 
4.4%
Other values (68) 173410
32.9%
Distinct3510
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Memory size9.5 MiB
2025-05-12T01:41:45.682214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1858
Median length834
Mean length423.53563
Min length16

Characters and Unicode

Total characters8488501
Distinct characters95
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique925 ?
Unique (%)4.6%

Sample

1st rowInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.
2nd rowInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.
3rd rowInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.
4th rowInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.
5th rowInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.
ValueCountFrequency (%)
and 84153
 
7.0%
of 55530
 
4.6%
the 45933
 
3.8%
to 26303
 
2.2%
in 25017
 
2.1%
for 16378
 
1.4%
or 14665
 
1.2%
a 12302
 
1.0%
hours 12005
 
1.0%
prerequisite 11145
 
0.9%
Other values (12276) 903061
74.9%
2025-05-12T01:41:46.155045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1186867
14.0%
e 752554
 
8.9%
i 590604
 
7.0%
n 564372
 
6.6%
t 555303
 
6.5%
a 539230
 
6.4%
o 521903
 
6.1%
s 490670
 
5.8%
r 467397
 
5.5%
c 292502
 
3.4%
Other values (85) 2527099
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8488501
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1186867
14.0%
e 752554
 
8.9%
i 590604
 
7.0%
n 564372
 
6.6%
t 555303
 
6.5%
a 539230
 
6.4%
o 521903
 
6.1%
s 490670
 
5.8%
r 467397
 
5.5%
c 292502
 
3.4%
Other values (85) 2527099
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8488501
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1186867
14.0%
e 752554
 
8.9%
i 590604
 
7.0%
n 564372
 
6.6%
t 555303
 
6.5%
a 539230
 
6.4%
o 521903
 
6.1%
s 490670
 
5.8%
r 467397
 
5.5%
c 292502
 
3.4%
Other values (85) 2527099
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8488501
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1186867
14.0%
e 752554
 
8.9%
i 590604
 
7.0%
n 564372
 
6.6%
t 555303
 
6.5%
a 539230
 
6.4%
o 521903
 
6.1%
s 490670
 
5.8%
r 467397
 
5.5%
c 292502
 
3.4%
Other values (85) 2527099
29.8%

Credit Hours
Categorical

High correlation  Imbalance 

Distinct49
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
3 hours.
10378 
4 hours.
3533 
3 OR 4 hours.
1732 
2 hours.
1049 
1 hours.
1049 
Other values (44)
2301 

Length

Max length15
Median length8
Mean length8.946662
Min length8

Characters and Unicode

Total characters179309
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row3 hours.
2nd row3 hours.
3rd row3 hours.
4th row3 hours.
5th row3 hours.

Common Values

ValueCountFrequency (%)
3 hours. 10378
51.8%
4 hours. 3533
 
17.6%
3 OR 4 hours. 1732
 
8.6%
2 hours. 1049
 
5.2%
1 hours. 1049
 
5.2%
2 OR 4 hours. 461
 
2.3%
2 TO 4 hours. 246
 
1.2%
1 TO 4 hours. 192
 
1.0%
0 TO 4 hours. 161
 
0.8%
1 OR 2 hours. 152
 
0.8%
Other values (39) 1089
 
5.4%

Length

2025-05-12T01:41:46.273085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hours 20042
42.1%
3 12446
26.2%
4 6531
 
13.7%
or 2549
 
5.4%
2 2080
 
4.4%
1 1768
 
3.7%
to 1194
 
2.5%
0 351
 
0.7%
5 316
 
0.7%
8 60
 
0.1%
Other values (12) 233
 
0.5%

Most occurring characters

ValueCountFrequency (%)
27528
15.4%
o 20312
11.3%
r 20164
11.2%
. 20163
11.2%
h 20042
11.2%
s 20042
11.2%
u 20042
11.2%
3 12488
7.0%
4 6539
 
3.6%
O 3473
 
1.9%
Other values (11) 8516
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 179309
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
27528
15.4%
o 20312
11.3%
r 20164
11.2%
. 20163
11.2%
h 20042
11.2%
s 20042
11.2%
u 20042
11.2%
3 12488
7.0%
4 6539
 
3.6%
O 3473
 
1.9%
Other values (11) 8516
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 179309
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
27528
15.4%
o 20312
11.3%
r 20164
11.2%
. 20163
11.2%
h 20042
11.2%
s 20042
11.2%
u 20042
11.2%
3 12488
7.0%
4 6539
 
3.6%
O 3473
 
1.9%
Other values (11) 8516
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 179309
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
27528
15.4%
o 20312
11.3%
r 20164
11.2%
. 20163
11.2%
h 20042
11.2%
s 20042
11.2%
u 20042
11.2%
3 12488
7.0%
4 6539
 
3.6%
O 3473
 
1.9%
Other values (11) 8516
 
4.7%

Section Info
Text

Missing 

Distinct2515
Distinct (%)15.7%
Missing4055
Missing (%)20.2%
Memory size2.5 MiB
2025-05-12T01:41:46.599933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length586
Median length339
Mean length100.45087
Min length12

Characters and Unicode

Total characters1605908
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique649 ?
Unique (%)4.1%

Sample

1st rowSame as LLS 200.
2nd rowMay be repeated to a maximum of 12 hours.
3rd rowPrerequisite: One of MATH 220, MATH 221, MATH 234.
4th rowSame as TSM 430. 2 undergraduate hours. 2 graduate hours.
5th row3 undergraduate hours. 4 graduate hours. Credit is not given for both ABE 436 and TSM 438. Prerequisite: PHYS 211.
ValueCountFrequency (%)
or 12637
 
4.7%
hours 11522
 
4.3%
prerequisite 11103
 
4.1%
and 8771
 
3.3%
credit 8245
 
3.1%
of 7382
 
2.8%
graduate 6773
 
2.5%
for 6108
 
2.3%
4 5661
 
2.1%
math 5489
 
2.1%
Other values (1765) 184014
68.7%
2025-05-12T01:41:47.072913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
251824
15.7%
e 136661
 
8.5%
r 119426
 
7.4%
t 91376
 
5.7%
o 87139
 
5.4%
i 79755
 
5.0%
a 72407
 
4.5%
n 69074
 
4.3%
s 65890
 
4.1%
u 57440
 
3.6%
Other values (66) 574916
35.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1605908
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
251824
15.7%
e 136661
 
8.5%
r 119426
 
7.4%
t 91376
 
5.7%
o 87139
 
5.4%
i 79755
 
5.0%
a 72407
 
4.5%
n 69074
 
4.3%
s 65890
 
4.1%
u 57440
 
3.6%
Other values (66) 574916
35.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1605908
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
251824
15.7%
e 136661
 
8.5%
r 119426
 
7.4%
t 91376
 
5.7%
o 87139
 
5.4%
i 79755
 
5.0%
a 72407
 
4.5%
n 69074
 
4.3%
s 65890
 
4.1%
u 57440
 
3.6%
Other values (66) 574916
35.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1605908
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
251824
15.7%
e 136661
 
8.5%
r 119426
 
7.4%
t 91376
 
5.7%
o 87139
 
5.4%
i 79755
 
5.0%
a 72407
 
4.5%
n 69074
 
4.3%
s 65890
 
4.1%
u 57440
 
3.6%
Other values (66) 574916
35.8%

Degree Attributes
Text

Missing 

Distinct59
Distinct (%)1.2%
Missing14998
Missing (%)74.8%
Memory size972.7 KiB
2025-05-12T01:41:47.229436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length99
Median length94
Mean length45.292823
Min length21

Characters and Unicode

Total characters228457
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.2%

Sample

1st rowSocial & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.
2nd rowSocial & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.
3rd rowSocial & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.
4th rowSocial & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.
5th rowSocial & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.
ValueCountFrequency (%)
9070
22.2%
course 5044
12.4%
sci 3465
 
8.5%
studies 1850
 
4.5%
cultural 1850
 
4.5%
and 1845
 
4.5%
beh 1809
 
4.4%
humanities 1594
 
3.9%
social 1449
 
3.5%
soc 1089
 
2.7%
Other values (28) 11771
28.8%
2025-05-12T01:41:47.535267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35792
15.7%
i 18225
 
8.0%
e 16248
 
7.1%
s 13584
 
5.9%
c 13426
 
5.9%
t 13100
 
5.7%
u 12953
 
5.7%
o 11625
 
5.1%
a 10371
 
4.5%
n 9555
 
4.2%
Other values (34) 73578
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 228457
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
35792
15.7%
i 18225
 
8.0%
e 16248
 
7.1%
s 13584
 
5.9%
c 13426
 
5.9%
t 13100
 
5.7%
u 12953
 
5.7%
o 11625
 
5.1%
a 10371
 
4.5%
n 9555
 
4.2%
Other values (34) 73578
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 228457
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
35792
15.7%
i 18225
 
8.0%
e 16248
 
7.1%
s 13584
 
5.9%
c 13426
 
5.9%
t 13100
 
5.7%
u 12953
 
5.7%
o 11625
 
5.1%
a 10371
 
4.5%
n 9555
 
4.2%
Other values (34) 73578
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 228457
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
35792
15.7%
i 18225
 
8.0%
e 16248
 
7.1%
s 13584
 
5.9%
c 13426
 
5.9%
t 13100
 
5.7%
u 12953
 
5.7%
o 11625
 
5.1%
a 10371
 
4.5%
n 9555
 
4.2%
Other values (34) 73578
32.2%

Schedule Information
Text

Missing 

Distinct174
Distinct (%)5.2%
Missing16724
Missing (%)83.4%
Memory size1.1 MiB
2025-05-12T01:41:47.751214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length659
Median length334
Mean length116.7824
Min length11

Characters and Unicode

Total characters387484
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)0.8%

Sample

1st rowStudents must register for one lecture and one discussion.
2nd rowStudents must register for one lecture and one discussion.
3rd rowStudents must register for one discussion and one lecture section.
4th rowStudents must register for one discussion and one lecture section.
5th rowStudents must register for one lecture/discussion section and one lab/discussion section. Not for graduate credit.
ValueCountFrequency (%)
one 4037
 
6.8%
and 3319
 
5.6%
for 3235
 
5.5%
students 3116
 
5.3%
section 2513
 
4.3%
must 2402
 
4.1%
register 2178
 
3.7%
lecture 2157
 
3.7%
the 1517
 
2.6%
discussion 1472
 
2.5%
Other values (572) 33149
56.1%
2025-05-12T01:41:48.115684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
55808
14.4%
e 40842
 
10.5%
t 29207
 
7.5%
n 28081
 
7.2%
s 27620
 
7.1%
i 24790
 
6.4%
o 24142
 
6.2%
r 24028
 
6.2%
u 14761
 
3.8%
a 14585
 
3.8%
Other values (64) 103620
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 387484
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
55808
14.4%
e 40842
 
10.5%
t 29207
 
7.5%
n 28081
 
7.2%
s 27620
 
7.1%
i 24790
 
6.4%
o 24142
 
6.2%
r 24028
 
6.2%
u 14761
 
3.8%
a 14585
 
3.8%
Other values (64) 103620
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 387484
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
55808
14.4%
e 40842
 
10.5%
t 29207
 
7.5%
n 28081
 
7.2%
s 27620
 
7.1%
i 24790
 
6.4%
o 24142
 
6.2%
r 24028
 
6.2%
u 14761
 
3.8%
a 14585
 
3.8%
Other values (64) 103620
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 387484
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
55808
14.4%
e 40842
 
10.5%
t 29207
 
7.5%
n 28081
 
7.2%
s 27620
 
7.1%
i 24790
 
6.4%
o 24142
 
6.2%
r 24028
 
6.2%
u 14761
 
3.8%
a 14585
 
3.8%
Other values (64) 103620
26.7%

CRN
Real number (ℝ)

Distinct8096
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53465.403
Minimum10051
Maximum78820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:41:48.234039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10051
5-th percentile31048
Q137449.75
median55467.5
Q367918.75
95-th percentile74665.9
Maximum78820
Range68769
Interquartile range (IQR)30469

Descriptive statistics

Standard deviation15369.237
Coefficient of variation (CV)0.28746136
Kurtosis-1.4406977
Mean53465.403
Median Absolute Deviation (MAD)14775.5
Skewness-0.11581966
Sum1.0715536 × 109
Variance2.3621346 × 108
MonotonicityNot monotonic
2025-05-12T01:41:48.371086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53068 28
 
0.1%
53066 24
 
0.1%
55158 22
 
0.1%
53203 19
 
0.1%
32838 15
 
0.1%
65138 13
 
0.1%
35803 12
 
0.1%
56271 12
 
0.1%
75873 12
 
0.1%
63134 11
 
0.1%
Other values (8086) 19874
99.2%
ValueCountFrequency (%)
10051 2
< 0.1%
11484 1
 
< 0.1%
15375 1
 
< 0.1%
20680 1
 
< 0.1%
22099 2
< 0.1%
27106 1
 
< 0.1%
27137 1
 
< 0.1%
29649 4
< 0.1%
29650 2
< 0.1%
29656 4
< 0.1%
ValueCountFrequency (%)
78820 1
< 0.1%
78770 1
< 0.1%
78742 1
< 0.1%
78664 1
< 0.1%
78662 1
< 0.1%
78661 1
< 0.1%
78660 1
< 0.1%
78644 2
< 0.1%
78636 1
< 0.1%
78635 1
< 0.1%

Section_x
Text

Missing 

Distinct1443
Distinct (%)7.6%
Missing1061
Missing (%)5.3%
Memory size1.1 MiB
2025-05-12T01:41:48.862650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.094779
Min length1

Characters and Unicode

Total characters39761
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique473 ?
Unique (%)2.5%

Sample

1st rowAD1
2nd rowAD2
3rd rowAD3
4th rowAD4
5th rowAD5
ValueCountFrequency (%)
a 3633
 
19.1%
al1 1490
 
7.8%
b 892
 
4.7%
c 447
 
2.4%
onl 365
 
1.9%
d 321
 
1.7%
e 247
 
1.3%
f 187
 
1.0%
ad1 186
 
1.0%
al2 183
 
1.0%
Other values (1429) 11030
58.1%
2025-05-12T01:41:49.556621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 9907
24.9%
1 4154
10.4%
L 3375
 
8.5%
D 3375
 
8.5%
B 2333
 
5.9%
O 1471
 
3.7%
3 1471
 
3.7%
2 1416
 
3.6%
C 1405
 
3.5%
E 1315
 
3.3%
Other values (28) 9539
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 9907
24.9%
1 4154
10.4%
L 3375
 
8.5%
D 3375
 
8.5%
B 2333
 
5.9%
O 1471
 
3.7%
3 1471
 
3.7%
2 1416
 
3.6%
C 1405
 
3.5%
E 1315
 
3.3%
Other values (28) 9539
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 9907
24.9%
1 4154
10.4%
L 3375
 
8.5%
D 3375
 
8.5%
B 2333
 
5.9%
O 1471
 
3.7%
3 1471
 
3.7%
2 1416
 
3.6%
C 1405
 
3.5%
E 1315
 
3.3%
Other values (28) 9539
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 9907
24.9%
1 4154
10.4%
L 3375
 
8.5%
D 3375
 
8.5%
B 2333
 
5.9%
O 1471
 
3.7%
3 1471
 
3.7%
2 1416
 
3.6%
C 1405
 
3.5%
E 1315
 
3.3%
Other values (28) 9539
24.0%

Status Code
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
A
20038 
P
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20042
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 20038
> 99.9%
P 4
 
< 0.1%

Length

2025-05-12T01:41:49.714731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T01:41:49.795672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 20038
> 99.9%
p 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 20038
> 99.9%
P 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 20038
> 99.9%
P 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 20038
> 99.9%
P 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 20038
> 99.9%
P 4
 
< 0.1%

Part of Term
Categorical

Imbalance  Missing 

Distinct4
Distinct (%)< 0.1%
Missing359
Missing (%)1.8%
Memory size1.1 MiB
1
16873 
A
 
1324
B
 
1190
LF
 
296

Length

Max length2
Median length1
Mean length1.0150384
Min length1

Characters and Unicode

Total characters19979
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 16873
84.2%
A 1324
 
6.6%
B 1190
 
5.9%
LF 296
 
1.5%
(Missing) 359
 
1.8%

Length

2025-05-12T01:41:49.892478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T01:41:50.860412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 16873
85.7%
a 1324
 
6.7%
b 1190
 
6.0%
lf 296
 
1.5%

Most occurring characters

ValueCountFrequency (%)
1 16873
84.5%
A 1324
 
6.6%
B 1190
 
6.0%
L 296
 
1.5%
F 296
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19979
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 16873
84.5%
A 1324
 
6.6%
B 1190
 
6.0%
L 296
 
1.5%
F 296
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19979
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 16873
84.5%
A 1324
 
6.6%
B 1190
 
6.0%
L 296
 
1.5%
F 296
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19979
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 16873
84.5%
A 1324
 
6.6%
B 1190
 
6.0%
L 296
 
1.5%
F 296
 
1.5%

Section Title
Text

Missing 

Distinct636
Distinct (%)47.5%
Missing18703
Missing (%)93.3%
Memory size689.6 KiB
2025-05-12T01:41:51.115558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length25
Mean length23.230022
Min length4

Characters and Unicode

Total characters31105
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique361 ?
Unique (%)27.0%

Sample

1st rowWater in the Global Environ
2nd rowAccounting Analysis I A
3rd rowAccounting Analysis I B
4th rowAccounting Analysis I A
5th rowAccounting Analysis I B
ValueCountFrequency (%)
212
 
4.7%
and 112
 
2.5%
in 100
 
2.2%
for 82
 
1.8%
of 77
 
1.7%
data 76
 
1.7%
learning 55
 
1.2%
design 53
 
1.2%
analysis 50
 
1.1%
the 50
 
1.1%
Other values (953) 3626
80.7%
2025-05-12T01:41:51.470660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3152
 
10.1%
e 2400
 
7.7%
n 2340
 
7.5%
i 2209
 
7.1%
a 2037
 
6.5%
o 1886
 
6.1%
t 1758
 
5.7%
r 1648
 
5.3%
s 1415
 
4.5%
c 1192
 
3.8%
Other values (63) 11068
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31105
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3152
 
10.1%
e 2400
 
7.7%
n 2340
 
7.5%
i 2209
 
7.1%
a 2037
 
6.5%
o 1886
 
6.1%
t 1758
 
5.7%
r 1648
 
5.3%
s 1415
 
4.5%
c 1192
 
3.8%
Other values (63) 11068
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31105
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3152
 
10.1%
e 2400
 
7.7%
n 2340
 
7.5%
i 2209
 
7.1%
a 2037
 
6.5%
o 1886
 
6.1%
t 1758
 
5.7%
r 1648
 
5.3%
s 1415
 
4.5%
c 1192
 
3.8%
Other values (63) 11068
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31105
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3152
 
10.1%
e 2400
 
7.7%
n 2340
 
7.5%
i 2209
 
7.1%
a 2037
 
6.5%
o 1886
 
6.1%
t 1758
 
5.7%
r 1648
 
5.3%
s 1415
 
4.5%
c 1192
 
3.8%
Other values (63) 11068
35.6%

Section Credit Hours
Categorical

High correlation  Missing 

Distinct8
Distinct (%)0.2%
Missing15449
Missing (%)77.1%
Memory size1.2 MiB
3 hours
2211 
4 hours
1358 
1 hours
515 
2 hours
489 
5 hours
 
9
Other values (3)
 
11

Length

Max length9
Median length7
Mean length7.0037013
Min length7

Characters and Unicode

Total characters32168
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row3 hours
2nd row3 hours
3rd row3 hours
4th row3 hours
5th row3 hours

Common Values

ValueCountFrequency (%)
3 hours 2211
 
11.0%
4 hours 1358
 
6.8%
1 hours 515
 
2.6%
2 hours 489
 
2.4%
5 hours 9
 
< 0.1%
4.5 hours 8
 
< 0.1%
8 hours 2
 
< 0.1%
12 hours 1
 
< 0.1%
(Missing) 15449
77.1%

Length

2025-05-12T01:41:51.579739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T01:41:51.682294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hours 4593
50.0%
3 2211
24.1%
4 1358
 
14.8%
1 515
 
5.6%
2 489
 
5.3%
5 9
 
0.1%
4.5 8
 
0.1%
8 2
 
< 0.1%
12 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
4593
14.3%
o 4593
14.3%
h 4593
14.3%
u 4593
14.3%
r 4593
14.3%
s 4593
14.3%
3 2211
6.9%
4 1366
 
4.2%
1 516
 
1.6%
2 490
 
1.5%
Other values (3) 27
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4593
14.3%
o 4593
14.3%
h 4593
14.3%
u 4593
14.3%
r 4593
14.3%
s 4593
14.3%
3 2211
6.9%
4 1366
 
4.2%
1 516
 
1.6%
2 490
 
1.5%
Other values (3) 27
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4593
14.3%
o 4593
14.3%
h 4593
14.3%
u 4593
14.3%
r 4593
14.3%
s 4593
14.3%
3 2211
6.9%
4 1366
 
4.2%
1 516
 
1.6%
2 490
 
1.5%
Other values (3) 27
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4593
14.3%
o 4593
14.3%
h 4593
14.3%
u 4593
14.3%
r 4593
14.3%
s 4593
14.3%
3 2211
6.9%
4 1366
 
4.2%
1 516
 
1.6%
2 490
 
1.5%
Other values (3) 27
 
0.1%

Section Status
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
A
20038 
P
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20042
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 20038
> 99.9%
P 4
 
< 0.1%

Length

2025-05-12T01:41:51.792410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T01:41:51.847885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 20038
> 99.9%
p 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 20038
> 99.9%
P 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 20038
> 99.9%
P 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 20038
> 99.9%
P 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 20038
> 99.9%
P 4
 
< 0.1%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
UNKNOWN
5045 
Open (Restricted)
4408 
Open
4224 
Closed
4107 
CrossListOpen (Restricted)
1286 

Length

Max length26
Median length13
Mean length9.8723181
Min length4

Characters and Unicode

Total characters197861
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOpen
2nd rowOpen
3rd rowOpen
4th rowOpen
5th rowClosed

Common Values

ValueCountFrequency (%)
UNKNOWN 5045
25.2%
Open (Restricted) 4408
22.0%
Open 4224
21.1%
Closed 4107
20.5%
CrossListOpen (Restricted) 1286
 
6.4%
CrossListOpen 972
 
4.8%

Length

2025-05-12T01:41:51.920257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T01:41:52.002121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
open 8632
33.5%
restricted 5694
22.1%
unknown 5045
19.6%
closed 4107
16.0%
crosslistopen 2258
 
8.8%

Most occurring characters

ValueCountFrequency (%)
e 26385
13.3%
s 16575
 
8.4%
O 15935
 
8.1%
N 15135
 
7.6%
t 13646
 
6.9%
p 10890
 
5.5%
n 10890
 
5.5%
d 9801
 
5.0%
i 7952
 
4.0%
r 7952
 
4.0%
Other values (12) 62700
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 197861
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 26385
13.3%
s 16575
 
8.4%
O 15935
 
8.1%
N 15135
 
7.6%
t 13646
 
6.9%
p 10890
 
5.5%
n 10890
 
5.5%
d 9801
 
5.0%
i 7952
 
4.0%
r 7952
 
4.0%
Other values (12) 62700
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 197861
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 26385
13.3%
s 16575
 
8.4%
O 15935
 
8.1%
N 15135
 
7.6%
t 13646
 
6.9%
p 10890
 
5.5%
n 10890
 
5.5%
d 9801
 
5.0%
i 7952
 
4.0%
r 7952
 
4.0%
Other values (12) 62700
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 197861
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 26385
13.3%
s 16575
 
8.4%
O 15935
 
8.1%
N 15135
 
7.6%
t 13646
 
6.9%
p 10890
 
5.5%
n 10890
 
5.5%
d 9801
 
5.0%
i 7952
 
4.0%
r 7952
 
4.0%
Other values (12) 62700
31.7%

Type
Categorical

High correlation 

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Lecture-Discussion
6612 
Online
4571 
Lecture
2883 
Discussion/Recitation
2179 
Online Lecture
1678 
Other values (13)
2119 

Length

Max length25
Median length21
Mean length13.202475
Min length4

Characters and Unicode

Total characters264604
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowOnline Discussion
2nd rowOnline Discussion
3rd rowOnline Discussion
4th rowOnline Discussion
5th rowOnline Discussion

Common Values

ValueCountFrequency (%)
Lecture-Discussion 6612
33.0%
Online 4571
22.8%
Lecture 2883
14.4%
Discussion/Recitation 2179
 
10.9%
Online Lecture 1678
 
8.4%
Online Discussion 827
 
4.1%
Laboratory 760
 
3.8%
Online Lab 208
 
1.0%
Laboratory-Discussion 140
 
0.7%
Practice 64
 
0.3%
Other values (8) 120
 
0.6%

Length

2025-05-12T01:41:52.116407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
online 7314
32.0%
lecture-discussion 6612
29.0%
lecture 4591
20.1%
discussion/recitation 2179
 
9.5%
discussion 857
 
3.8%
laboratory 760
 
3.3%
lab 208
 
0.9%
laboratory-discussion 140
 
0.6%
practice 64
 
0.3%
studio 34
 
0.1%
Other values (8) 69
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 32074
12.1%
i 31378
11.9%
s 29371
11.1%
n 26678
10.1%
c 23345
8.8%
u 21031
7.9%
t 16580
 
6.3%
o 13836
 
5.2%
r 13105
 
5.0%
L 12311
 
4.7%
Other values (23) 44895
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 264604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 32074
12.1%
i 31378
11.9%
s 29371
11.1%
n 26678
10.1%
c 23345
8.8%
u 21031
7.9%
t 16580
 
6.3%
o 13836
 
5.2%
r 13105
 
5.0%
L 12311
 
4.7%
Other values (23) 44895
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 264604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 32074
12.1%
i 31378
11.9%
s 29371
11.1%
n 26678
10.1%
c 23345
8.8%
u 21031
7.9%
t 16580
 
6.3%
o 13836
 
5.2%
r 13105
 
5.0%
L 12311
 
4.7%
Other values (23) 44895
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 264604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 32074
12.1%
i 31378
11.9%
s 29371
11.1%
n 26678
10.1%
c 23345
8.8%
u 21031
7.9%
t 16580
 
6.3%
o 13836
 
5.2%
r 13105
 
5.0%
L 12311
 
4.7%
Other values (23) 44895
17.0%
Distinct71
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2025-05-12T01:41:52.278275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9327412
Min length1

Characters and Unicode

Total characters58778
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.1%

Sample

1st rowOD
2nd rowOD
3rd rowOD
4th rowOD
5th rowOD
ValueCountFrequency (%)
lcd 6413
32.0%
onl 4385
21.9%
lec 2851
14.2%
dis 2178
 
10.9%
olc 1678
 
8.4%
od 827
 
4.1%
lab 706
 
3.5%
olb 208
 
1.0%
lbd 140
 
0.7%
e1 70
 
0.3%
Other values (61) 586
 
2.9%
2025-05-12T01:41:52.555194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 16443
28.0%
C 10965
18.7%
D 9590
16.3%
O 7128
12.1%
N 4411
 
7.5%
E 3044
 
5.2%
S 2418
 
4.1%
I 2186
 
3.7%
B 1108
 
1.9%
A 706
 
1.2%
Other values (18) 779
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 16443
28.0%
C 10965
18.7%
D 9590
16.3%
O 7128
12.1%
N 4411
 
7.5%
E 3044
 
5.2%
S 2418
 
4.1%
I 2186
 
3.7%
B 1108
 
1.9%
A 706
 
1.2%
Other values (18) 779
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 16443
28.0%
C 10965
18.7%
D 9590
16.3%
O 7128
12.1%
N 4411
 
7.5%
E 3044
 
5.2%
S 2418
 
4.1%
I 2186
 
3.7%
B 1108
 
1.9%
A 706
 
1.2%
Other values (18) 779
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 16443
28.0%
C 10965
18.7%
D 9590
16.3%
O 7128
12.1%
N 4411
 
7.5%
E 3044
 
5.2%
S 2418
 
4.1%
I 2186
 
3.7%
B 1108
 
1.9%
A 706
 
1.2%
Other values (18) 779
 
1.3%

Start Time
Categorical

Distinct36
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
11:00 AM
2935 
ARRANGED
2900 
02:00 PM
2328 
01:00 PM
1582 
10:00 AM
1352 
Other values (31)
8945 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters160336
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st row09:00 AM
2nd row12:00 PM
3rd row01:00 PM
4th row10:00 AM
5th row11:00 AM

Common Values

ValueCountFrequency (%)
11:00 AM 2935
14.6%
ARRANGED 2900
14.5%
02:00 PM 2328
11.6%
01:00 PM 1582
7.9%
10:00 AM 1352
6.7%
09:30 AM 1347
6.7%
09:00 AM 1291
 
6.4%
12:30 PM 1206
 
6.0%
12:00 PM 961
 
4.8%
03:30 PM 866
 
4.3%
Other values (26) 3274
16.3%

Length

2025-05-12T01:41:52.660721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pm 9539
25.7%
am 7603
20.4%
11:00 2935
 
7.9%
arranged 2900
 
7.8%
02:00 2328
 
6.3%
01:00 1582
 
4.3%
10:00 1353
 
3.6%
09:30 1347
 
3.6%
09:00 1292
 
3.5%
12:30 1206
 
3.2%
Other values (24) 5099
13.7%

Most occurring characters

ValueCountFrequency (%)
0 42334
26.4%
: 17142
10.7%
17142
10.7%
M 17142
10.7%
A 13403
 
8.4%
1 11219
 
7.0%
P 9539
 
5.9%
R 5800
 
3.6%
3 5690
 
3.5%
2 4543
 
2.8%
Other values (10) 16382
 
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 160336
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 42334
26.4%
: 17142
10.7%
17142
10.7%
M 17142
10.7%
A 13403
 
8.4%
1 11219
 
7.0%
P 9539
 
5.9%
R 5800
 
3.6%
3 5690
 
3.5%
2 4543
 
2.8%
Other values (10) 16382
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 160336
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 42334
26.4%
: 17142
10.7%
17142
10.7%
M 17142
10.7%
A 13403
 
8.4%
1 11219
 
7.0%
P 9539
 
5.9%
R 5800
 
3.6%
3 5690
 
3.5%
2 4543
 
2.8%
Other values (10) 16382
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 160336
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 42334
26.4%
: 17142
10.7%
17142
10.7%
M 17142
10.7%
A 13403
 
8.4%
1 11219
 
7.0%
P 9539
 
5.9%
R 5800
 
3.6%
3 5690
 
3.5%
2 4543
 
2.8%
Other values (10) 16382
 
10.2%

End Time
Date

Missing 

Distinct108
Distinct (%)0.6%
Missing2900
Missing (%)14.5%
Memory size156.7 KiB
Minimum2025-05-12 07:50:00
Maximum2025-05-12 23:20:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-12T01:41:52.761647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:52.884542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Days of Week
Categorical

Missing 

Distinct28
Distinct (%)0.2%
Missing2855
Missing (%)14.2%
Memory size1.1 MiB
TR
5161 
MW
2997 
MWF
2582 
F
1792 
R
1221 
Other values (23)
3434 

Length

Max length5
Median length4
Mean length1.8180602
Min length1

Characters and Unicode

Total characters31247
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
TR 5161
25.8%
MW 2997
15.0%
MWF 2582
12.9%
F 1792
 
8.9%
R 1221
 
6.1%
W 1131
 
5.6%
T 963
 
4.8%
M 805
 
4.0%
WF 111
 
0.6%
S 96
 
0.5%
Other values (18) 328
 
1.6%
(Missing) 2855
14.2%

Length

2025-05-12T01:41:53.004108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tr 5161
30.0%
mw 2997
17.4%
mwf 2582
15.0%
f 1792
 
10.4%
r 1221
 
7.1%
w 1131
 
6.6%
t 963
 
5.6%
m 805
 
4.7%
wf 111
 
0.6%
s 96
 
0.6%
Other values (18) 328
 
1.9%

Most occurring characters

ValueCountFrequency (%)
W 7038
22.5%
M 6621
21.2%
R 6522
20.9%
T 6350
20.3%
F 4615
14.8%
S 99
 
0.3%
U 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31247
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 7038
22.5%
M 6621
21.2%
R 6522
20.9%
T 6350
20.3%
F 4615
14.8%
S 99
 
0.3%
U 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31247
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 7038
22.5%
M 6621
21.2%
R 6522
20.9%
T 6350
20.3%
F 4615
14.8%
S 99
 
0.3%
U 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31247
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 7038
22.5%
M 6621
21.2%
R 6522
20.9%
T 6350
20.3%
F 4615
14.8%
S 99
 
0.3%
U 2
 
< 0.1%

Room
Text

Missing 

Distinct426
Distinct (%)3.5%
Missing7703
Missing (%)38.4%
Memory size968.0 KiB
2025-05-12T01:41:53.400247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.3489748
Min length1

Characters and Unicode

Total characters41323
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)0.3%

Sample

1st row2063
2nd row3057
3rd row2011
4th row2007
5th row2011
ValueCountFrequency (%)
100 239
 
1.9%
103 192
 
1.6%
1002 171
 
1.4%
215 166
 
1.3%
141 159
 
1.3%
111 149
 
1.2%
112 138
 
1.1%
2001 131
 
1.1%
166 131
 
1.1%
119 130
 
1.1%
Other values (417) 10737
87.0%
2025-05-12T01:41:53.929186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10083
24.4%
0 7799
18.9%
2 6379
15.4%
3 5407
13.1%
4 2752
 
6.7%
5 2103
 
5.1%
6 1799
 
4.4%
7 1451
 
3.5%
9 1179
 
2.9%
8 831
 
2.0%
Other values (25) 1540
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41323
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 10083
24.4%
0 7799
18.9%
2 6379
15.4%
3 5407
13.1%
4 2752
 
6.7%
5 2103
 
5.1%
6 1799
 
4.4%
7 1451
 
3.5%
9 1179
 
2.9%
8 831
 
2.0%
Other values (25) 1540
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41323
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 10083
24.4%
0 7799
18.9%
2 6379
15.4%
3 5407
13.1%
4 2752
 
6.7%
5 2103
 
5.1%
6 1799
 
4.4%
7 1451
 
3.5%
9 1179
 
2.9%
8 831
 
2.0%
Other values (25) 1540
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41323
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 10083
24.4%
0 7799
18.9%
2 6379
15.4%
3 5407
13.1%
4 2752
 
6.7%
5 2103
 
5.1%
6 1799
 
4.4%
7 1451
 
3.5%
9 1179
 
2.9%
8 831
 
2.0%
Other values (25) 1540
 
3.7%

Building
Text

Missing 

Distinct98
Distinct (%)0.8%
Missing7703
Missing (%)38.4%
Memory size1.1 MiB
2025-05-12T01:41:54.233292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length27
Mean length18.202691
Min length6

Characters and Unicode

Total characters224603
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowBusiness Instructional Fac
2nd rowBusiness Instructional Fac
3rd rowBusiness Instructional Fac
4th rowBusiness Instructional Fac
5th rowBusiness Instructional Fac
ValueCountFrequency (%)
hall 4943
 
15.2%
building 1768
 
5.4%
instructional 1659
 
5.1%
business 1189
 
3.7%
fac 1189
 
3.7%
laboratory 1079
 
3.3%
bldg 948
 
2.9%
gregory 845
 
2.6%
844
 
2.6%
david 763
 
2.3%
Other values (168) 17269
53.1%
2025-05-12T01:41:54.653513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 21837
 
9.7%
20229
 
9.0%
i 16780
 
7.5%
a 16060
 
7.2%
r 14808
 
6.6%
n 14708
 
6.5%
e 11943
 
5.3%
o 11173
 
5.0%
t 9885
 
4.4%
s 9385
 
4.2%
Other values (48) 77795
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 224603
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 21837
 
9.7%
20229
 
9.0%
i 16780
 
7.5%
a 16060
 
7.2%
r 14808
 
6.6%
n 14708
 
6.5%
e 11943
 
5.3%
o 11173
 
5.0%
t 9885
 
4.4%
s 9385
 
4.2%
Other values (48) 77795
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 224603
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 21837
 
9.7%
20229
 
9.0%
i 16780
 
7.5%
a 16060
 
7.2%
r 14808
 
6.6%
n 14708
 
6.5%
e 11943
 
5.3%
o 11173
 
5.0%
t 9885
 
4.4%
s 9385
 
4.2%
Other values (48) 77795
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 224603
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 21837
 
9.7%
20229
 
9.0%
i 16780
 
7.5%
a 16060
 
7.2%
r 14808
 
6.6%
n 14708
 
6.5%
e 11943
 
5.3%
o 11173
 
5.0%
t 9885
 
4.4%
s 9385
 
4.2%
Other values (48) 77795
34.6%
Distinct6424
Distinct (%)32.1%
Missing48
Missing (%)0.2%
Memory size1.4 MiB
2025-05-12T01:41:54.936157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length306
Median length286
Mean length15.737721
Min length5

Characters and Unicode

Total characters314660
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2874 ?
Unique (%)14.4%

Sample

1st rowBoonsripaisal, S
2nd rowKang, Y
3rd rowWang, Y
4th rowBoonsripaisal, S
5th rowKang, Y
ValueCountFrequency (%)
j 2494
 
4.7%
a 2054
 
3.9%
m 2033
 
3.8%
s 1532
 
2.9%
c 1301
 
2.5%
d 1225
 
2.3%
k 941
 
1.8%
r 907
 
1.7%
l 897
 
1.7%
e 817
 
1.5%
Other values (7135) 38669
73.1%
2025-05-12T01:41:55.348279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32876
 
10.4%
, 31040
 
9.9%
e 20199
 
6.4%
a 19825
 
6.3%
n 15864
 
5.0%
r 15365
 
4.9%
i 13854
 
4.4%
o 13680
 
4.3%
; 11046
 
3.5%
l 10474
 
3.3%
Other values (47) 130437
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 314660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
32876
 
10.4%
, 31040
 
9.9%
e 20199
 
6.4%
a 19825
 
6.3%
n 15864
 
5.0%
r 15365
 
4.9%
i 13854
 
4.4%
o 13680
 
4.3%
; 11046
 
3.5%
l 10474
 
3.3%
Other values (47) 130437
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 314660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
32876
 
10.4%
, 31040
 
9.9%
e 20199
 
6.4%
a 19825
 
6.3%
n 15864
 
5.0%
r 15365
 
4.9%
i 13854
 
4.4%
o 13680
 
4.3%
; 11046
 
3.5%
l 10474
 
3.3%
Other values (47) 130437
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 314660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
32876
 
10.4%
, 31040
 
9.9%
e 20199
 
6.4%
a 19825
 
6.3%
n 15864
 
5.0%
r 15365
 
4.9%
i 13854
 
4.4%
o 13680
 
4.3%
; 11046
 
3.5%
l 10474
 
3.3%
Other values (47) 130437
41.5%

source_file
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2021-fa.csv
2865 
2023-fa.csv
2841 
2022-fa.csv
2807 
2020-fa.csv
2404 
2022-sp.csv
2384 
Other values (3)
6741 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters220462
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-fa.csv
2nd row2020-fa.csv
3rd row2020-fa.csv
4th row2020-fa.csv
5th row2020-fa.csv

Common Values

ValueCountFrequency (%)
2021-fa.csv 2865
14.3%
2023-fa.csv 2841
14.2%
2022-fa.csv 2807
14.0%
2020-fa.csv 2404
12.0%
2022-sp.csv 2384
11.9%
2023-sp.csv 2300
11.5%
2021-sp.csv 2261
11.3%
2020-sp.csv 2180
10.9%

Length

2025-05-12T01:41:55.452698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-12T01:41:55.537964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2021-fa.csv 2865
14.3%
2023-fa.csv 2841
14.2%
2022-fa.csv 2807
14.0%
2020-fa.csv 2404
12.0%
2022-sp.csv 2384
11.9%
2023-sp.csv 2300
11.5%
2021-sp.csv 2261
11.3%
2020-sp.csv 2180
10.9%

Most occurring characters

ValueCountFrequency (%)
2 45275
20.5%
s 29167
13.2%
0 24626
11.2%
- 20042
9.1%
v 20042
9.1%
c 20042
9.1%
. 20042
9.1%
f 10917
 
5.0%
a 10917
 
5.0%
p 9125
 
4.1%
Other values (2) 10267
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 220462
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 45275
20.5%
s 29167
13.2%
0 24626
11.2%
- 20042
9.1%
v 20042
9.1%
c 20042
9.1%
. 20042
9.1%
f 10917
 
5.0%
a 10917
 
5.0%
p 9125
 
4.1%
Other values (2) 10267
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 220462
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 45275
20.5%
s 29167
13.2%
0 24626
11.2%
- 20042
9.1%
v 20042
9.1%
c 20042
9.1%
. 20042
9.1%
f 10917
 
5.0%
a 10917
 
5.0%
p 9125
 
4.1%
Other values (2) 10267
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 220462
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 45275
20.5%
s 29167
13.2%
0 24626
11.2%
- 20042
9.1%
v 20042
9.1%
c 20042
9.1%
. 20042
9.1%
f 10917
 
5.0%
a 10917
 
5.0%
p 9125
 
4.1%
Other values (2) 10267
 
4.7%
Distinct157
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2025-05-12T01:41:55.899896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.3756112
Min length2

Characters and Unicode

Total characters67654
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.1%

Sample

1st rowAAS
2nd rowAAS
3rd rowAAS
4th rowAAS
5th rowAAS
ValueCountFrequency (%)
econ 931
 
4.6%
badm 923
 
4.6%
accy 787
 
3.9%
mcb 701
 
3.5%
psyc 690
 
3.4%
math 613
 
3.1%
cs 591
 
2.9%
ece 590
 
2.9%
cmn 519
 
2.6%
fin 496
 
2.5%
Other values (147) 13201
65.9%
2025-05-12T01:41:56.344667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 9645
14.3%
S 7533
11.1%
A 6473
 
9.6%
E 6448
 
9.5%
M 4456
 
6.6%
N 3894
 
5.8%
H 3607
 
5.3%
T 3083
 
4.6%
L 2755
 
4.1%
I 2745
 
4.1%
Other values (14) 17015
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 9645
14.3%
S 7533
11.1%
A 6473
 
9.6%
E 6448
 
9.5%
M 4456
 
6.6%
N 3894
 
5.8%
H 3607
 
5.3%
T 3083
 
4.6%
L 2755
 
4.1%
I 2745
 
4.1%
Other values (14) 17015
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 9645
14.3%
S 7533
11.1%
A 6473
 
9.6%
E 6448
 
9.5%
M 4456
 
6.6%
N 3894
 
5.8%
H 3607
 
5.3%
T 3083
 
4.6%
L 2755
 
4.1%
I 2745
 
4.1%
Other values (14) 17015
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 9645
14.3%
S 7533
11.1%
A 6473
 
9.6%
E 6448
 
9.5%
M 4456
 
6.6%
N 3894
 
5.8%
H 3607
 
5.3%
T 3083
 
4.6%
L 2755
 
4.1%
I 2745
 
4.1%
Other values (14) 17015
25.2%

Course
Unsupported

Missing  Rejected  Unsupported 

Missing20042
Missing (%)100.0%
Memory size156.7 KiB
Distinct3456
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-05-12T01:41:56.603525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length25
Mean length23.625387
Min length3

Characters and Unicode

Total characters473500
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique953 ?
Unique (%)4.8%

Sample

1st rowIntro Asian American Studies
2nd rowIntro Asian American Studies
3rd rowIntro Asian American Studies
4th rowIntro Asian American Studies
5th rowIntro Asian American Studies
ValueCountFrequency (%)
2490
 
3.7%
and 2166
 
3.2%
to 2072
 
3.1%
intro 1671
 
2.5%
of 1604
 
2.4%
in 1468
 
2.2%
i 902
 
1.3%
introduction 898
 
1.3%
design 768
 
1.1%
ii 742
 
1.1%
Other values (2635) 52660
78.1%
2025-05-12T01:41:56.990081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47399
 
10.0%
i 36860
 
7.8%
n 35886
 
7.6%
e 35198
 
7.4%
o 31697
 
6.7%
t 30696
 
6.5%
a 28874
 
6.1%
r 25059
 
5.3%
s 23387
 
4.9%
c 20479
 
4.3%
Other values (66) 157965
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 473500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
47399
 
10.0%
i 36860
 
7.8%
n 35886
 
7.6%
e 35198
 
7.4%
o 31697
 
6.7%
t 30696
 
6.5%
a 28874
 
6.1%
r 25059
 
5.3%
s 23387
 
4.9%
c 20479
 
4.3%
Other values (66) 157965
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 473500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
47399
 
10.0%
i 36860
 
7.8%
n 35886
 
7.6%
e 35198
 
7.4%
o 31697
 
6.7%
t 30696
 
6.5%
a 28874
 
6.1%
r 25059
 
5.3%
s 23387
 
4.9%
c 20479
 
4.3%
Other values (66) 157965
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 473500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
47399
 
10.0%
i 36860
 
7.8%
n 35886
 
7.6%
e 35198
 
7.4%
o 31697
 
6.7%
t 30696
 
6.5%
a 28874
 
6.1%
r 25059
 
5.3%
s 23387
 
4.9%
c 20479
 
4.3%
Other values (66) 157965
33.4%

A+
Real number (ℝ)

Zeros 

Distinct282
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.946961
Minimum0
Maximum929
Zeros5049
Zeros (%)25.2%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:41:57.110254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q312
95-th percentile42
Maximum929
Range929
Interquartile range (IQR)12

Descriptive statistics

Standard deviation32.910474
Coefficient of variation (CV)2.754715
Kurtosis154.32368
Mean11.946961
Median Absolute Deviation (MAD)4
Skewness10.246011
Sum239441
Variance1083.0993
MonotonicityNot monotonic
2025-05-12T01:41:57.242495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5049
25.2%
1 1617
 
8.1%
2 1366
 
6.8%
3 1218
 
6.1%
4 1141
 
5.7%
5 952
 
4.8%
6 827
 
4.1%
7 685
 
3.4%
8 593
 
3.0%
9 545
 
2.7%
Other values (272) 6049
30.2%
ValueCountFrequency (%)
0 5049
25.2%
1 1617
 
8.1%
2 1366
 
6.8%
3 1218
 
6.1%
4 1141
 
5.7%
5 952
 
4.8%
6 827
 
4.1%
7 685
 
3.4%
8 593
 
3.0%
9 545
 
2.7%
ValueCountFrequency (%)
929 1
< 0.1%
724 1
< 0.1%
688 1
< 0.1%
687 1
< 0.1%
681 1
< 0.1%
662 1
< 0.1%
632 1
< 0.1%
596 1
< 0.1%
582 1
< 0.1%
564 1
< 0.1%

A
Real number (ℝ)

Distinct381
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.474853
Minimum0
Maximum1034
Zeros162
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:41:57.369722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median13
Q323
95-th percentile75
Maximum1034
Range1034
Interquartile range (IQR)16

Descriptive statistics

Standard deviation48.652155
Coefficient of variation (CV)1.9878426
Kurtosis84.842429
Mean24.474853
Median Absolute Deviation (MAD)7
Skewness7.792155
Sum490525
Variance2367.0322
MonotonicityNot monotonic
2025-05-12T01:41:57.506464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 1004
 
5.0%
8 989
 
4.9%
6 943
 
4.7%
7 930
 
4.6%
9 890
 
4.4%
10 838
 
4.2%
4 832
 
4.2%
3 756
 
3.8%
11 749
 
3.7%
12 666
 
3.3%
Other values (371) 11445
57.1%
ValueCountFrequency (%)
0 162
 
0.8%
1 314
 
1.6%
2 569
2.8%
3 756
3.8%
4 832
4.2%
5 1004
5.0%
6 943
4.7%
7 930
4.6%
8 989
4.9%
9 890
4.4%
ValueCountFrequency (%)
1034 1
< 0.1%
888 1
< 0.1%
833 1
< 0.1%
814 1
< 0.1%
775 1
< 0.1%
763 1
< 0.1%
760 1
< 0.1%
743 1
< 0.1%
737 1
< 0.1%
727 2
< 0.1%

A-
Real number (ℝ)

High correlation  Zeros 

Distinct159
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8574494
Minimum0
Maximum351
Zeros2918
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:41:57.631576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q39
95-th percentile26
Maximum351
Range351
Interquartile range (IQR)7

Descriptive statistics

Standard deviation14.276769
Coefficient of variation (CV)1.8169725
Kurtosis105.13302
Mean7.8574494
Median Absolute Deviation (MAD)3
Skewness8.0057737
Sum157479
Variance203.82614
MonotonicityNot monotonic
2025-05-12T01:41:57.761558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2918
14.6%
3 2004
10.0%
2 1844
9.2%
4 1738
 
8.7%
1 1630
 
8.1%
5 1562
 
7.8%
6 1361
 
6.8%
7 1050
 
5.2%
8 880
 
4.4%
9 672
 
3.4%
Other values (149) 4383
21.9%
ValueCountFrequency (%)
0 2918
14.6%
1 1630
8.1%
2 1844
9.2%
3 2004
10.0%
4 1738
8.7%
5 1562
7.8%
6 1361
6.8%
7 1050
 
5.2%
8 880
 
4.4%
9 672
 
3.4%
ValueCountFrequency (%)
351 1
< 0.1%
300 1
< 0.1%
285 1
< 0.1%
283 1
< 0.1%
281 1
< 0.1%
272 1
< 0.1%
245 1
< 0.1%
239 1
< 0.1%
232 1
< 0.1%
228 1
< 0.1%

B+
Real number (ℝ)

High correlation  Zeros 

Distinct115
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3894821
Minimum0
Maximum210
Zeros3856
Zeros (%)19.2%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:41:57.906466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile19
Maximum210
Range210
Interquartile range (IQR)5

Descriptive statistics

Standard deviation9.5119455
Coefficient of variation (CV)1.764909
Kurtosis76.26774
Mean5.3894821
Median Absolute Deviation (MAD)2
Skewness6.5920151
Sum108016
Variance90.477107
MonotonicityNot monotonic
2025-05-12T01:41:58.028915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3856
19.2%
1 3030
15.1%
2 2539
12.7%
3 2147
10.7%
4 1632
8.1%
5 1305
 
6.5%
6 935
 
4.7%
7 681
 
3.4%
8 582
 
2.9%
9 437
 
2.2%
Other values (105) 2898
14.5%
ValueCountFrequency (%)
0 3856
19.2%
1 3030
15.1%
2 2539
12.7%
3 2147
10.7%
4 1632
8.1%
5 1305
 
6.5%
6 935
 
4.7%
7 681
 
3.4%
8 582
 
2.9%
9 437
 
2.2%
ValueCountFrequency (%)
210 1
< 0.1%
198 1
< 0.1%
184 1
< 0.1%
172 1
< 0.1%
171 1
< 0.1%
170 1
< 0.1%
167 1
< 0.1%
159 1
< 0.1%
132 1
< 0.1%
131 2
< 0.1%

B
Real number (ℝ)

High correlation  Zeros 

Distinct126
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5029937
Minimum0
Maximum211
Zeros3123
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:41:58.157138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile25
Maximum211
Range211
Interquartile range (IQR)6

Descriptive statistics

Standard deviation11.829967
Coefficient of variation (CV)1.8191571
Kurtosis39.561899
Mean6.5029937
Median Absolute Deviation (MAD)2
Skewness5.1282876
Sum130333
Variance139.94812
MonotonicityNot monotonic
2025-05-12T01:41:58.285955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3263
16.3%
0 3123
15.6%
2 2605
13.0%
3 2081
10.4%
4 1604
8.0%
5 1269
 
6.3%
6 965
 
4.8%
7 732
 
3.7%
8 569
 
2.8%
9 454
 
2.3%
Other values (116) 3377
16.8%
ValueCountFrequency (%)
0 3123
15.6%
1 3263
16.3%
2 2605
13.0%
3 2081
10.4%
4 1604
8.0%
5 1269
 
6.3%
6 965
 
4.8%
7 732
 
3.7%
8 569
 
2.8%
9 454
 
2.3%
ValueCountFrequency (%)
211 1
< 0.1%
173 1
< 0.1%
166 1
< 0.1%
163 1
< 0.1%
161 1
< 0.1%
155 1
< 0.1%
154 1
< 0.1%
150 1
< 0.1%
148 1
< 0.1%
145 1
< 0.1%

B-
Real number (ℝ)

High correlation  Zeros 

Distinct66
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6657519
Minimum0
Maximum104
Zeros8033
Zeros (%)40.1%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:41:58.412857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile11
Maximum104
Range104
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.2893044
Coefficient of variation (CV)1.9841698
Kurtosis43.935536
Mean2.6657519
Median Absolute Deviation (MAD)1
Skewness5.1931805
Sum53427
Variance27.976741
MonotonicityNot monotonic
2025-05-12T01:41:58.553451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8033
40.1%
1 3823
19.1%
2 2355
 
11.8%
3 1596
 
8.0%
4 1010
 
5.0%
5 694
 
3.5%
6 461
 
2.3%
7 334
 
1.7%
8 276
 
1.4%
9 207
 
1.0%
Other values (56) 1253
 
6.3%
ValueCountFrequency (%)
0 8033
40.1%
1 3823
19.1%
2 2355
 
11.8%
3 1596
 
8.0%
4 1010
 
5.0%
5 694
 
3.5%
6 461
 
2.3%
7 334
 
1.7%
8 276
 
1.4%
9 207
 
1.0%
ValueCountFrequency (%)
104 1
< 0.1%
99 1
< 0.1%
82 2
< 0.1%
75 1
< 0.1%
72 1
< 0.1%
66 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%
60 1
< 0.1%
58 2
< 0.1%

C+
Real number (ℝ)

High correlation  Zeros 

Distinct57
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7830556
Minimum0
Maximum69
Zeros10484
Zeros (%)52.3%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:41:58.693438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile8
Maximum69
Range69
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.0844872
Coefficient of variation (CV)2.2907235
Kurtosis47.056389
Mean1.7830556
Median Absolute Deviation (MAD)0
Skewness5.613616
Sum35736
Variance16.683036
MonotonicityNot monotonic
2025-05-12T01:41:58.832974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10484
52.3%
1 3732
 
18.6%
2 1997
 
10.0%
3 1119
 
5.6%
4 666
 
3.3%
5 458
 
2.3%
6 280
 
1.4%
7 226
 
1.1%
8 170
 
0.8%
9 121
 
0.6%
Other values (47) 789
 
3.9%
ValueCountFrequency (%)
0 10484
52.3%
1 3732
 
18.6%
2 1997
 
10.0%
3 1119
 
5.6%
4 666
 
3.3%
5 458
 
2.3%
6 280
 
1.4%
7 226
 
1.1%
8 170
 
0.8%
9 121
 
0.6%
ValueCountFrequency (%)
69 1
 
< 0.1%
63 1
 
< 0.1%
60 2
< 0.1%
59 1
 
< 0.1%
56 1
 
< 0.1%
55 1
 
< 0.1%
54 2
< 0.1%
53 2
< 0.1%
51 1
 
< 0.1%
50 3
< 0.1%

C
Real number (ℝ)

High correlation  Zeros 

Distinct58
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3523101
Minimum0
Maximum74
Zeros9463
Zeros (%)47.2%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:41:58.974078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile11
Maximum74
Range74
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.5372927
Coefficient of variation (CV)2.3539807
Kurtosis30.694982
Mean2.3523101
Median Absolute Deviation (MAD)1
Skewness4.9652447
Sum47145
Variance30.661611
MonotonicityNot monotonic
2025-05-12T01:41:59.117258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9463
47.2%
1 4026
20.1%
2 2146
 
10.7%
3 1204
 
6.0%
4 714
 
3.6%
5 420
 
2.1%
6 354
 
1.8%
7 233
 
1.2%
8 199
 
1.0%
9 138
 
0.7%
Other values (48) 1145
 
5.7%
ValueCountFrequency (%)
0 9463
47.2%
1 4026
20.1%
2 2146
 
10.7%
3 1204
 
6.0%
4 714
 
3.6%
5 420
 
2.1%
6 354
 
1.8%
7 233
 
1.2%
8 199
 
1.0%
9 138
 
0.7%
ValueCountFrequency (%)
74 1
 
< 0.1%
56 3
 
< 0.1%
55 2
 
< 0.1%
54 1
 
< 0.1%
53 6
< 0.1%
52 1
 
< 0.1%
51 4
 
< 0.1%
50 11
0.1%
49 7
< 0.1%
48 3
 
< 0.1%

C-
Real number (ℝ)

High correlation  Zeros 

Distinct38
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99002096
Minimum0
Maximum52
Zeros13344
Zeros (%)66.6%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:41:59.248408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum52
Range52
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.668558
Coefficient of variation (CV)2.6954561
Kurtosis52.529104
Mean0.99002096
Median Absolute Deviation (MAD)0
Skewness6.0411676
Sum19842
Variance7.1212017
MonotonicityNot monotonic
2025-05-12T01:41:59.369749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 13344
66.6%
1 3251
 
16.2%
2 1355
 
6.8%
3 647
 
3.2%
4 395
 
2.0%
5 243
 
1.2%
6 154
 
0.8%
7 109
 
0.5%
8 95
 
0.5%
9 63
 
0.3%
Other values (28) 386
 
1.9%
ValueCountFrequency (%)
0 13344
66.6%
1 3251
 
16.2%
2 1355
 
6.8%
3 647
 
3.2%
4 395
 
2.0%
5 243
 
1.2%
6 154
 
0.8%
7 109
 
0.5%
8 95
 
0.5%
9 63
 
0.3%
ValueCountFrequency (%)
52 1
 
< 0.1%
38 1
 
< 0.1%
37 3
< 0.1%
36 3
< 0.1%
35 1
 
< 0.1%
34 2
 
< 0.1%
33 6
< 0.1%
32 1
 
< 0.1%
29 3
< 0.1%
28 2
 
< 0.1%

D+
Real number (ℝ)

Zeros 

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49191697
Minimum0
Maximum34
Zeros15896
Zeros (%)79.3%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:41:59.494510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum34
Range34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.560854
Coefficient of variation (CV)3.1730029
Kurtosis65.497827
Mean0.49191697
Median Absolute Deviation (MAD)0
Skewness6.6198462
Sum9859
Variance2.4362652
MonotonicityNot monotonic
2025-05-12T01:41:59.605873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 15896
79.3%
1 2274
 
11.3%
2 794
 
4.0%
3 404
 
2.0%
4 208
 
1.0%
5 110
 
0.5%
6 80
 
0.4%
7 63
 
0.3%
8 43
 
0.2%
9 38
 
0.2%
Other values (18) 132
 
0.7%
ValueCountFrequency (%)
0 15896
79.3%
1 2274
 
11.3%
2 794
 
4.0%
3 404
 
2.0%
4 208
 
1.0%
5 110
 
0.5%
6 80
 
0.4%
7 63
 
0.3%
8 43
 
0.2%
9 38
 
0.2%
ValueCountFrequency (%)
34 1
 
< 0.1%
30 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 2
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
20 3
< 0.1%
19 4
< 0.1%
18 2
< 0.1%

D
Real number (ℝ)

High correlation  Zeros 

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69289492
Minimum0
Maximum30
Zeros14328
Zeros (%)71.5%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:41:59.711085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum30
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8165829
Coefficient of variation (CV)2.6217293
Kurtosis39.796323
Mean0.69289492
Median Absolute Deviation (MAD)0
Skewness5.2885936
Sum13887
Variance3.2999735
MonotonicityNot monotonic
2025-05-12T01:41:59.829118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 14328
71.5%
1 3084
 
15.4%
2 1098
 
5.5%
3 537
 
2.7%
4 292
 
1.5%
5 175
 
0.9%
6 128
 
0.6%
7 90
 
0.4%
8 74
 
0.4%
9 58
 
0.3%
Other values (18) 178
 
0.9%
ValueCountFrequency (%)
0 14328
71.5%
1 3084
 
15.4%
2 1098
 
5.5%
3 537
 
2.7%
4 292
 
1.5%
5 175
 
0.9%
6 128
 
0.6%
7 90
 
0.4%
8 74
 
0.4%
9 58
 
0.3%
ValueCountFrequency (%)
30 1
 
< 0.1%
26 1
 
< 0.1%
25 2
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 4
< 0.1%
20 4
< 0.1%
19 2
 
< 0.1%
18 7
< 0.1%

D-
Real number (ℝ)

Zeros 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31069754
Minimum0
Maximum19
Zeros16846
Zeros (%)84.1%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:41:59.940837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0290013
Coefficient of variation (CV)3.3119069
Kurtosis54.183739
Mean0.31069754
Median Absolute Deviation (MAD)0
Skewness6.179608
Sum6227
Variance1.0588437
MonotonicityNot monotonic
2025-05-12T01:42:00.039821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 16846
84.1%
1 1960
 
9.8%
2 606
 
3.0%
3 253
 
1.3%
4 132
 
0.7%
5 83
 
0.4%
6 49
 
0.2%
8 27
 
0.1%
7 26
 
0.1%
9 16
 
0.1%
Other values (7) 44
 
0.2%
ValueCountFrequency (%)
0 16846
84.1%
1 1960
 
9.8%
2 606
 
3.0%
3 253
 
1.3%
4 132
 
0.7%
5 83
 
0.4%
6 49
 
0.2%
7 26
 
0.1%
8 27
 
0.1%
9 16
 
0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
15 4
 
< 0.1%
14 3
 
< 0.1%
13 4
 
< 0.1%
12 8
 
< 0.1%
11 8
 
< 0.1%
10 16
0.1%
9 16
0.1%
8 27
0.1%
7 26
0.1%

F
Real number (ℝ)

Zeros 

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3974653
Minimum0
Maximum75
Zeros11338
Zeros (%)56.6%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:42:00.174491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum75
Range75
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.4193305
Coefficient of variation (CV)2.4468089
Kurtosis74.155054
Mean1.3974653
Median Absolute Deviation (MAD)0
Skewness6.8010419
Sum28008
Variance11.691821
MonotonicityNot monotonic
2025-05-12T01:42:00.314263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11338
56.6%
1 3784
 
18.9%
2 1873
 
9.3%
3 1006
 
5.0%
4 520
 
2.6%
5 376
 
1.9%
6 233
 
1.2%
7 172
 
0.9%
8 143
 
0.7%
9 85
 
0.4%
Other values (43) 512
 
2.6%
ValueCountFrequency (%)
0 11338
56.6%
1 3784
 
18.9%
2 1873
 
9.3%
3 1006
 
5.0%
4 520
 
2.6%
5 376
 
1.9%
6 233
 
1.2%
7 172
 
0.9%
8 143
 
0.7%
9 85
 
0.4%
ValueCountFrequency (%)
75 1
 
< 0.1%
74 1
 
< 0.1%
64 1
 
< 0.1%
59 1
 
< 0.1%
53 1
 
< 0.1%
51 1
 
< 0.1%
50 2
 
< 0.1%
46 1
 
< 0.1%
45 1
 
< 0.1%
43 5
< 0.1%

W
Real number (ℝ)

Zeros 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18171839
Minimum0
Maximum16
Zeros17728
Zeros (%)88.5%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:42:00.436903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.68275432
Coefficient of variation (CV)3.7572109
Kurtosis88.531762
Mean0.18171839
Median Absolute Deviation (MAD)0
Skewness7.5347235
Sum3642
Variance0.46615347
MonotonicityNot monotonic
2025-05-12T01:42:00.531513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 17728
88.5%
1 1669
 
8.3%
2 369
 
1.8%
3 134
 
0.7%
4 52
 
0.3%
5 34
 
0.2%
6 18
 
0.1%
8 10
 
< 0.1%
7 8
 
< 0.1%
9 8
 
< 0.1%
Other values (5) 12
 
0.1%
ValueCountFrequency (%)
0 17728
88.5%
1 1669
 
8.3%
2 369
 
1.8%
3 134
 
0.7%
4 52
 
0.3%
5 34
 
0.2%
6 18
 
0.1%
7 8
 
< 0.1%
8 10
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
13 1
 
< 0.1%
12 4
 
< 0.1%
11 2
 
< 0.1%
10 4
 
< 0.1%
9 8
 
< 0.1%
8 10
 
< 0.1%
7 8
 
< 0.1%
6 18
0.1%
5 34
0.2%

Average Grade
Real number (ℝ)

High correlation 

Distinct213
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4830451
Minimum1.14
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:42:00.642722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.14
5-th percentile2.78
Q13.28
median3.55
Q33.77
95-th percentile3.93
Maximum4
Range2.86
Interquartile range (IQR)0.49

Descriptive statistics

Standard deviation0.36030058
Coefficient of variation (CV)0.10344413
Kurtosis0.90916618
Mean3.4830451
Median Absolute Deviation (MAD)0.24
Skewness-0.96764788
Sum69807.19
Variance0.12981651
MonotonicityNot monotonic
2025-05-12T01:42:00.771591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.83 331
 
1.7%
3.67 280
 
1.4%
3.76 275
 
1.4%
3.86 275
 
1.4%
3.62 270
 
1.3%
3.92 269
 
1.3%
3.64 267
 
1.3%
3.91 267
 
1.3%
3.79 265
 
1.3%
3.81 254
 
1.3%
Other values (203) 17289
86.3%
ValueCountFrequency (%)
1.14 1
 
< 0.1%
1.21 1
 
< 0.1%
1.41 1
 
< 0.1%
1.64 1
 
< 0.1%
1.75 1
 
< 0.1%
1.76 2
< 0.1%
1.78 3
< 0.1%
1.79 1
 
< 0.1%
1.83 1
 
< 0.1%
1.85 1
 
< 0.1%
ValueCountFrequency (%)
4 2
 
< 0.1%
3.99 26
 
0.1%
3.98 81
 
0.4%
3.97 177
0.9%
3.96 214
1.1%
3.95 191
1.0%
3.94 213
1.1%
3.93 218
1.1%
3.92 269
1.3%
3.91 267
1.3%
Distinct4035
Distinct (%)20.2%
Missing37
Missing (%)0.2%
Memory size1.4 MiB
2025-05-12T01:42:01.129647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length22
Mean length15.99935
Min length6

Characters and Unicode

Total characters320067
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique982 ?
Unique (%)4.9%

Sample

1st rowBoonsripaisal, Simon
2nd rowKang, Yoonjung
3rd rowWang, Yu
4th rowBoonsripaisal, Simon
5th rowKang, Yoonjung
ValueCountFrequency (%)
m 1809
 
3.4%
a 1409
 
2.6%
j 1408
 
2.6%
l 1030
 
1.9%
r 726
 
1.4%
c 667
 
1.2%
e 644
 
1.2%
s 635
 
1.2%
d 510
 
0.9%
k 473
 
0.9%
Other values (5051) 44374
82.7%
2025-05-12T01:42:01.728144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
33680
 
10.5%
a 27581
 
8.6%
e 24738
 
7.7%
n 21105
 
6.6%
, 20005
 
6.3%
i 18942
 
5.9%
r 17114
 
5.3%
o 13937
 
4.4%
l 12780
 
4.0%
s 9880
 
3.1%
Other values (47) 120305
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 320067
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
33680
 
10.5%
a 27581
 
8.6%
e 24738
 
7.7%
n 21105
 
6.6%
, 20005
 
6.3%
i 18942
 
5.9%
r 17114
 
5.3%
o 13937
 
4.4%
l 12780
 
4.0%
s 9880
 
3.1%
Other values (47) 120305
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 320067
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
33680
 
10.5%
a 27581
 
8.6%
e 24738
 
7.7%
n 21105
 
6.6%
, 20005
 
6.3%
i 18942
 
5.9%
r 17114
 
5.3%
o 13937
 
4.4%
l 12780
 
4.0%
s 9880
 
3.1%
Other values (47) 120305
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 320067
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
33680
 
10.5%
a 27581
 
8.6%
e 24738
 
7.7%
n 21105
 
6.6%
, 20005
 
6.3%
i 18942
 
5.9%
r 17114
 
5.3%
o 13937
 
4.4%
l 12780
 
4.0%
s 9880
 
3.1%
Other values (47) 120305
37.6%

Course Subject
Unsupported

Missing  Rejected  Unsupported 

Missing20042
Missing (%)100.0%
Memory size156.7 KiB

Course Number
Unsupported

Missing  Rejected  Unsupported 

Missing20042
Missing (%)100.0%
Memory size156.7 KiB

Course Section
Text

Missing 

Distinct1310
Distinct (%)9.9%
Missing6848
Missing (%)34.2%
Memory size975.0 KiB
2025-05-12T01:42:02.259258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0507807
Min length1

Characters and Unicode

Total characters27058
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique416 ?
Unique (%)3.2%

Sample

1st rowAD1
2nd rowAD2
3rd rowAD3
4th rowAD4
5th rowAD5
ValueCountFrequency (%)
a 2419
 
18.3%
al1 961
 
7.3%
b 597
 
4.5%
c 286
 
2.2%
1 235
 
1.8%
d 195
 
1.5%
onl 185
 
1.4%
e 155
 
1.2%
ad1 129
 
1.0%
a1 118
 
0.9%
Other values (1296) 7914
60.0%
2025-05-12T01:42:02.934476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 6541
24.2%
1 3026
11.2%
D 2210
 
8.2%
L 2105
 
7.8%
B 1590
 
5.9%
2 1116
 
4.1%
3 1048
 
3.9%
C 911
 
3.4%
E 886
 
3.3%
O 884
 
3.3%
Other values (28) 6741
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27058
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 6541
24.2%
1 3026
11.2%
D 2210
 
8.2%
L 2105
 
7.8%
B 1590
 
5.9%
2 1116
 
4.1%
3 1048
 
3.9%
C 911
 
3.4%
E 886
 
3.3%
O 884
 
3.3%
Other values (28) 6741
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27058
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 6541
24.2%
1 3026
11.2%
D 2210
 
8.2%
L 2105
 
7.8%
B 1590
 
5.9%
2 1116
 
4.1%
3 1048
 
3.9%
C 911
 
3.4%
E 886
 
3.3%
O 884
 
3.3%
Other values (28) 6741
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27058
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 6541
24.2%
1 3026
11.2%
D 2210
 
8.2%
L 2105
 
7.8%
B 1590
 
5.9%
2 1116
 
4.1%
3 1048
 
3.9%
C 911
 
3.4%
E 886
 
3.3%
O 884
 
3.3%
Other values (28) 6741
24.9%

Sched Type
Categorical

High correlation 

Distinct17
Distinct (%)0.1%
Missing5
Missing (%)< 0.1%
Memory size1.1 MiB
LCD
6447 
ONL
4806 
LEC
3025 
DIS
2113 
OLC
1674 
Other values (12)
1972 

Length

Max length3
Median length3
Mean length2.9521885
Min length1

Characters and Unicode

Total characters59153
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOD
2nd rowOD
3rd rowOD
4th rowOD
5th rowOD

Common Values

ValueCountFrequency (%)
LCD 6447
32.2%
ONL 4806
24.0%
LEC 3025
15.1%
DIS 2113
 
10.5%
OLC 1674
 
8.4%
OD 849
 
4.2%
LAB 616
 
3.1%
OLB 195
 
1.0%
LBD 137
 
0.7%
PR 65
 
0.3%
Other values (7) 110
 
0.5%

Length

2025-05-12T01:42:03.135271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lcd 6447
32.2%
onl 4833
24.1%
lec 3025
15.1%
dis 2113
 
10.5%
olc 1674
 
8.4%
od 849
 
4.2%
lab 616
 
3.1%
olb 195
 
1.0%
lbd 137
 
0.7%
pr 65
 
0.3%
Other values (6) 83
 
0.4%

Most occurring characters

ValueCountFrequency (%)
L 16900
28.6%
C 11167
18.9%
D 9546
16.1%
O 7551
12.8%
N 4834
 
8.2%
E 3033
 
5.1%
S 2155
 
3.6%
I 2120
 
3.6%
B 948
 
1.6%
A 616
 
1.0%
Other values (10) 283
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 59153
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 16900
28.6%
C 11167
18.9%
D 9546
16.1%
O 7551
12.8%
N 4834
 
8.2%
E 3033
 
5.1%
S 2155
 
3.6%
I 2120
 
3.6%
B 948
 
1.6%
A 616
 
1.0%
Other values (10) 283
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 59153
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 16900
28.6%
C 11167
18.9%
D 9546
16.1%
O 7551
12.8%
N 4834
 
8.2%
E 3033
 
5.1%
S 2155
 
3.6%
I 2120
 
3.6%
B 948
 
1.6%
A 616
 
1.0%
Other values (10) 283
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 59153
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 16900
28.6%
C 11167
18.9%
D 9546
16.1%
O 7551
12.8%
N 4834
 
8.2%
E 3033
 
5.1%
S 2155
 
3.6%
I 2120
 
3.6%
B 948
 
1.6%
A 616
 
1.0%
Other values (10) 283
 
0.5%

A Range
Unsupported

Missing  Rejected  Unsupported 

Missing20042
Missing (%)100.0%
Memory size156.7 KiB

B Range
Unsupported

Missing  Rejected  Unsupported 

Missing20042
Missing (%)100.0%
Memory size156.7 KiB

C Range
Unsupported

Missing  Rejected  Unsupported 

Missing20042
Missing (%)100.0%
Memory size156.7 KiB

D Range
Unsupported

Missing  Rejected  Unsupported 

Missing20042
Missing (%)100.0%
Memory size156.7 KiB

Course
Real number (ℝ)

High correlation 

Distinct508
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean307.99736
Minimum100
Maximum798
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-05-12T01:42:03.316300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1196
median301
Q3437
95-th percentile571
Maximum798
Range698
Interquartile range (IQR)241

Descriptive statistics

Standard deviation156.10712
Coefficient of variation (CV)0.50684566
Kurtosis-0.99884377
Mean307.99736
Median Absolute Deviation (MAD)131
Skewness0.24380412
Sum6172883
Variance24369.434
MonotonicityNot monotonic
2025-05-12T01:42:03.622724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1211
 
6.0%
101 1185
 
5.9%
102 361
 
1.8%
201 320
 
1.6%
103 285
 
1.4%
150 248
 
1.2%
250 245
 
1.2%
202 221
 
1.1%
199 216
 
1.1%
104 213
 
1.1%
Other values (498) 15537
77.5%
ValueCountFrequency (%)
100 1211
6.0%
101 1185
5.9%
102 361
 
1.8%
103 285
 
1.4%
104 213
 
1.1%
105 78
 
0.4%
106 16
 
0.1%
107 31
 
0.2%
108 21
 
0.1%
109 37
 
0.2%
ValueCountFrequency (%)
798 1
 
< 0.1%
797 6
 
< 0.1%
796 3
 
< 0.1%
795 2
 
< 0.1%
794 8
< 0.1%
792 18
0.1%
694 4
 
< 0.1%
686 3
 
< 0.1%
684 3
 
< 0.1%
682 7
 
< 0.1%

Section_y
Text

Missing 

Distinct477
Distinct (%)21.1%
Missing17781
Missing (%)88.7%
Memory size686.2 KiB
2025-05-12T01:42:04.036043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0592658
Min length1

Characters and Unicode

Total characters4656
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique275 ?
Unique (%)12.2%

Sample

1st rowAD1
2nd rowAD2
3rd rowAD3
4th rowAD4
5th rowAD5
ValueCountFrequency (%)
a 413
 
18.3%
al1 179
 
7.9%
b 97
 
4.3%
onl 67
 
3.0%
c 57
 
2.5%
d 44
 
1.9%
1 40
 
1.8%
e 33
 
1.5%
a3 23
 
1.0%
f 22
 
1.0%
Other values (467) 1286
56.9%
2025-05-12T01:42:04.614032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1126
24.2%
1 492
10.6%
L 437
 
9.4%
D 382
 
8.2%
B 237
 
5.1%
O 205
 
4.4%
3 185
 
4.0%
2 165
 
3.5%
C 162
 
3.5%
E 134
 
2.9%
Other values (27) 1131
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1126
24.2%
1 492
10.6%
L 437
 
9.4%
D 382
 
8.2%
B 237
 
5.1%
O 205
 
4.4%
3 185
 
4.0%
2 165
 
3.5%
C 162
 
3.5%
E 134
 
2.9%
Other values (27) 1131
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1126
24.2%
1 492
10.6%
L 437
 
9.4%
D 382
 
8.2%
B 237
 
5.1%
O 205
 
4.4%
3 185
 
4.0%
2 165
 
3.5%
C 162
 
3.5%
E 134
 
2.9%
Other values (27) 1131
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1126
24.2%
1 492
10.6%
L 437
 
9.4%
D 382
 
8.2%
B 237
 
5.1%
O 205
 
4.4%
3 185
 
4.0%
2 165
 
3.5%
C 162
 
3.5%
E 134
 
2.9%
Other values (27) 1131
24.3%

Interactions

2025-05-12T01:41:39.523056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:02.738632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:04.690213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:06.877585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:08.616507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:10.758035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:13.500550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:15.565823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:17.358280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:19.197622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:21.477307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:23.466875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:26.087014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:28.452785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:30.321681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:32.294901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:34.213742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:37.132230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:39.631019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-12T01:41:10.070301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:12.640171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:14.947474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:16.731390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:18.554301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:20.826855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:22.714348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:25.266428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:27.805813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:29.676106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:31.637714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:33.572980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:36.192516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:38.843536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:40.914166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:04.119367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:06.319695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:08.116921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:10.167160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:12.801993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:15.054242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:16.829576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:18.650864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:20.927403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:22.815626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:25.422086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:27.909084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:29.786705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:31.737083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:33.670151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:36.349229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:38.949881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:41.023448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:04.237002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:06.427188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:08.217556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:10.265755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:12.979125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:15.168662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:16.926656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:18.758714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:21.031633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:22.924726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:25.591043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:28.013154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:29.891581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:31.855538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:33.778569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:36.507065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:39.057666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:41.128812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:04.341541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:06.536446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:08.312893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:10.370894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:13.152210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:15.263748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:17.017939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:18.858425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:21.132580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:23.027504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:25.752200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:28.116109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:29.990926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:31.965873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:33.890582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:36.660064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:39.183591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:41.260236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:04.460343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:06.660118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:08.419497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:10.478656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:13.284777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:15.366094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:17.155022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:18.968825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:21.241955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:23.139341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:25.876869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:28.229411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:30.100057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:32.077379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:34.001794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:36.815253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:39.293317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:41.370397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:04.584259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:06.768871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:08.513952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:10.578514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:13.394495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:15.464901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:17.260639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:19.076385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:21.354971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:23.284937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:25.978626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:28.333361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:30.207888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:32.181181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:34.103023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:36.968927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-12T01:41:39.401235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-12T01:42:04.751228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AA+A-Average GradeBB+B-CC+C-CRNCourseCredit HoursDD+D-Days of WeekEnrollment StatusFNumberPart of TermSched TypeSection Credit HoursSection StatusStart TimeStatus CodeTermTypeWYearYearTermsource_file
A1.000-0.0030.2020.3040.2580.1130.0410.2040.0490.0620.0660.0450.0380.1960.0910.0710.0000.0230.1680.0450.0640.0570.0440.0000.0900.0000.0210.0590.1330.0140.0120.012
A+-0.0031.0000.1890.1420.0110.1570.1840.0830.1920.1880.031-0.1350.0300.1000.2010.1790.0000.0160.190-0.1350.0620.0430.0270.0000.0700.0000.0000.0440.1130.0170.0060.006
A-0.2020.1891.000-0.2290.3570.6610.5230.2030.4190.358-0.0660.0380.0620.1560.2860.2420.0180.0290.1990.0380.0170.0480.0480.0000.0620.0000.0000.0500.1510.0120.0070.007
Average Grade0.3040.142-0.2291.000-0.552-0.416-0.542-0.567-0.543-0.5000.1960.1140.125-0.449-0.412-0.3680.1300.093-0.4810.1140.1340.0870.1250.0000.0760.0000.0400.086-0.1650.0530.0410.041
B0.2580.0110.357-0.5521.0000.4940.4950.5860.4750.384-0.1520.0100.2190.4320.3290.2640.1730.0410.3260.0100.1180.0880.1430.0000.0650.0000.0230.0870.2210.0110.0120.012
B+0.1130.1570.661-0.4160.4941.0000.5970.3190.5440.411-0.1250.0580.0560.2300.3580.2740.0830.0320.2300.0580.0460.0600.0430.0000.0510.0000.0120.0620.1850.0110.0070.007
B-0.0410.1840.523-0.5420.4950.5971.0000.4310.5940.520-0.141-0.0500.0560.3140.4130.3450.0610.0420.319-0.0500.0160.0890.0000.0000.0250.0000.0000.0910.2060.0050.0000.000
C0.2040.0830.203-0.5670.5860.3190.4311.0000.4730.451-0.124-0.1130.2950.5020.3980.3330.1330.0550.410-0.1130.0940.1140.3880.0000.0550.0000.0080.1130.2290.0230.0240.024
C+0.0490.1920.419-0.5430.4750.5440.5940.4731.0000.525-0.129-0.1000.0410.3550.4630.3750.0610.0510.353-0.1000.0180.0940.0070.0000.0190.0000.0190.0960.2140.0020.0050.005
C-0.0620.1880.358-0.5000.3840.4110.5200.4510.5251.000-0.110-0.1480.0500.3790.4540.3930.0620.0530.384-0.1480.0200.0890.0000.0000.0140.0000.0050.0900.2220.0080.0110.011
CRN0.0660.031-0.0660.196-0.152-0.125-0.141-0.124-0.129-0.1101.0000.1310.153-0.074-0.090-0.0870.0790.063-0.0240.1310.1300.1660.1040.0000.0920.0000.1690.165-0.0130.0810.0830.083
Course0.045-0.1350.0380.1140.0100.058-0.050-0.113-0.100-0.1480.1311.0000.388-0.125-0.166-0.1530.3090.208-0.2481.0000.4210.1730.2710.0000.2070.0000.0860.166-0.0520.0090.0340.034
Credit Hours0.0380.0300.0620.1250.2190.0560.0560.2950.0410.0500.1530.3881.0000.1330.0450.0400.1820.2290.0000.3880.4130.2030.8200.0330.1060.0330.1350.1920.0330.0640.0660.066
D0.1960.1000.156-0.4490.4320.2300.3140.5020.3550.379-0.074-0.1250.1331.0000.3790.3380.0770.0410.402-0.1250.0650.0770.0510.0000.0270.0000.0190.0740.2320.0250.0200.020
D+0.0910.2010.286-0.4120.3290.3580.4130.3980.4630.454-0.090-0.1660.0450.3791.0000.4310.0400.0530.364-0.1660.0120.0760.0000.0000.0000.0000.0160.0780.2160.0180.0160.016
D-0.0710.1790.242-0.3680.2640.2740.3450.3330.3750.393-0.087-0.1530.0400.3380.4311.0000.0510.0500.330-0.1530.0250.0770.0190.0000.0190.0000.0000.0790.2160.0300.0210.021
Days of Week0.0000.0000.0180.1300.1730.0830.0610.1330.0610.0620.0790.3090.1820.0770.0400.0511.0000.1300.0310.3090.4870.1890.3540.0000.2120.0000.1160.1870.0270.0340.0510.051
Enrollment Status0.0230.0160.0290.0930.0410.0320.0420.0550.0510.0530.0630.2080.2290.0410.0530.0500.1301.0000.0350.2080.1130.1650.1700.0230.0990.0230.0440.1630.0310.3540.4600.460
F0.1680.1900.199-0.4810.3260.2300.3190.4100.3530.384-0.024-0.2480.0000.4020.3640.3300.0310.0351.000-0.2480.1210.0590.0000.0000.0700.0000.0000.0610.2540.0230.0200.020
Number0.045-0.1350.0380.1140.0100.058-0.050-0.113-0.100-0.1480.1311.0000.388-0.125-0.166-0.1530.3090.208-0.2481.0000.4210.1730.2710.0000.2070.0000.0860.166-0.0520.0090.0340.034
Part of Term0.0640.0620.0170.1340.1180.0460.0160.0940.0180.0200.1300.4210.4130.0650.0120.0250.4870.1130.1210.4211.0000.2000.3110.0290.3740.0290.0820.2030.0180.0090.0490.049
Sched Type0.0570.0430.0480.0870.0880.0600.0890.1140.0940.0890.1660.1730.2030.0770.0760.0770.1890.1650.0590.1730.2001.0000.2550.0000.1940.0000.0690.9450.0450.2530.2600.260
Section Credit Hours0.0440.0270.0480.1250.1430.0430.0000.3880.0070.0000.1040.2710.8200.0510.0000.0190.3540.1700.0000.2710.3110.2551.0000.0000.1400.0000.1230.2090.0220.0260.0500.050
Section Status0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0230.0000.0000.0290.0000.0001.0000.0000.8750.0000.0000.0000.0000.0090.009
Start Time0.0900.0700.0620.0760.0650.0510.0250.0550.0190.0140.0920.2070.1060.0270.0000.0190.2120.0990.0700.2070.3740.1940.1400.0001.0000.0000.0390.1910.0220.0620.0600.060
Status Code0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0230.0000.0000.0290.0000.0000.8750.0001.0000.0000.0000.0000.0000.0090.009
Term0.0210.0000.0000.0400.0230.0120.0000.0080.0190.0050.1690.0860.1350.0190.0160.0000.1160.0440.0000.0860.0820.0690.1230.0000.0390.0001.0000.0690.0400.0231.0001.000
Type0.0590.0440.0500.0860.0870.0620.0910.1130.0960.0900.1650.1660.1920.0740.0780.0790.1870.1630.0610.1660.2030.9450.2090.0000.1910.0000.0691.0000.0470.2500.2620.262
W0.1330.1130.151-0.1650.2210.1850.2060.2290.2140.222-0.013-0.0520.0330.2320.2160.2160.0270.0310.254-0.0520.0180.0450.0220.0000.0220.0000.0400.0471.0000.0590.0560.056
Year0.0140.0170.0120.0530.0110.0110.0050.0230.0020.0080.0810.0090.0640.0250.0180.0300.0340.3540.0230.0090.0090.2530.0260.0000.0620.0000.0230.2500.0591.0001.0001.000
YearTerm0.0120.0060.0070.0410.0120.0070.0000.0240.0050.0110.0830.0340.0660.0200.0160.0210.0510.4600.0200.0340.0490.2600.0500.0090.0600.0091.0000.2620.0561.0001.0001.000
source_file0.0120.0060.0070.0410.0120.0070.0000.0240.0050.0110.0830.0340.0660.0200.0160.0210.0510.4600.0200.0340.0490.2600.0500.0090.0600.0091.0000.2620.0561.0001.0001.000

Missing values

2025-05-12T01:41:41.686289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-12T01:41:42.148133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-12T01:41:42.667942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

YearTermYearTermSubject_xNumberNameDescriptionCredit HoursSection InfoDegree AttributesSchedule InformationCRNSection_xStatus CodePart of TermSection TitleSection Credit HoursSection StatusEnrollment StatusTypeType CodeStart TimeEnd TimeDays of WeekRoomBuildingInstructorssource_fileSubject_yCourseCourse TitleA+AA-B+BB-C+CC-D+DD-FWAverage GradePrimary InstructorCourse SubjectCourse NumberCourse SectionSched TypeA RangeB RangeC RangeD RangeCourseSection_y
02020FALL2020-faAAS100Intro Asian American StudiesInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.3 hours.NaNSocial & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.NaN41758AD1A1NaNNaNAOpenOnline DiscussionOD09:00 AM09:50 AMFNaNNaNBoonsripaisal, S2020-fa.csvAASNaNIntro Asian American Studies1094020001010103.55Boonsripaisal, SimonNaNNaNNaNODNaNNaNNaNNaN100NaN
12020FALL2020-faAAS100Intro Asian American StudiesInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.3 hours.NaNSocial & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.NaN47100AD2A1NaNNaNAOpenOnline DiscussionOD12:00 PM12:50 PMFNaNNaNKang, Y2020-fa.csvAASNaNIntro Asian American Studies2112251020010103.28Kang, YoonjungNaNNaNNaNODNaNNaNNaNNaN100NaN
22020FALL2020-faAAS100Intro Asian American StudiesInterdisciplinary introduction to the basic concepts and approaches in Asian American Studies. Surveys the various dimensions of Asian American experiences including history, social organization, literature, arts, and politics.3 hours.NaNSocial & Beh Sci - Soc Sci, and Cultural Studies - US Minority course.NaN47102AD3A1NaNNaNAOpenOnline DiscussionOD01:00 PM01:50 PMFNaNNaNWang, Y2020-fa.csvAASNaNIntro Asian American Studies1163300020000203.44Wang, YuNaNNaNNaNODNaNNaNNaNNaN100NaN
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200322023SPRING2023-spVM608Pathobiology IIIPathology, clinical pathology, and imaging of organ systems; epidemiology and food safety; and includes an integrative laboratory covering commonly encountered problems in infectious diseases. No graduate credit. 9 professional hours. Prerequisite: VM 607 and good standing in the veterinary professional curriculum, or consent of instructor.9 hours.No graduate credit. 9 professional hours. Prerequisite: VM 607 and good standing in the veterinary professional curriculum, or consent of instructor.NaNNaN55158AL1ABNaNNaNAOpen (Restricted)LectureLEC08:00 AM08:50 AMWNaNNaNBarger, A;Baumgartner, W;Colegrove-Calvey, K;Connolly, S;Delaney, M;Elston, C;Foreman, J;Hague, D;Hsiao, S;Johnson-Walker, Y;Landolfi, J;Roady, P;Rosser, M;Samuelson, J;Sander, W;Schnelle, A;Smith, B;Terio, K;Varga, C;Vieson, M2023-sp.csvVMNaNPathobiology III036005200390060002.89Vieson, MirandaNaNNaNAL1LECNaNNaNNaNNaN608NaN
200332023SPRING2023-spVM608Pathobiology IIIPathology, clinical pathology, and imaging of organ systems; epidemiology and food safety; and includes an integrative laboratory covering commonly encountered problems in infectious diseases. No graduate credit. 9 professional hours. Prerequisite: VM 607 and good standing in the veterinary professional curriculum, or consent of instructor.9 hours.No graduate credit. 9 professional hours. Prerequisite: VM 607 and good standing in the veterinary professional curriculum, or consent of instructor.NaNNaN55158AL1ABNaNNaNAOpen (Restricted)LectureLEC08:00 AM11:50 AMMFNaNNaNBarger, A;Baumgartner, W;Colegrove-Calvey, K;Connolly, S;Delaney, M;Elston, C;Foreman, J;Hague, D;Hsiao, S;Johnson-Walker, Y;Landolfi, J;Roady, P;Rosser, M;Samuelson, J;Sander, W;Schnelle, A;Smith, B;Terio, K;Varga, C;Vieson, M2023-sp.csvVMNaNPathobiology III036005200390060002.89Vieson, MirandaNaNNaNAL1LECNaNNaNNaNNaN608NaN
200342023SPRING2023-spVM608Pathobiology IIIPathology, clinical pathology, and imaging of organ systems; epidemiology and food safety; and includes an integrative laboratory covering commonly encountered problems in infectious diseases. No graduate credit. 9 professional hours. Prerequisite: VM 607 and good standing in the veterinary professional curriculum, or consent of instructor.9 hours.No graduate credit. 9 professional hours. Prerequisite: VM 607 and good standing in the veterinary professional curriculum, or consent of instructor.NaNNaN55158AL1ABNaNNaNAOpen (Restricted)LectureLEC09:00 AM11:50 AMTRNaNNaNBarger, A;Baumgartner, W;Colegrove-Calvey, K;Connolly, S;Delaney, M;Elston, C;Foreman, J;Hague, D;Hsiao, S;Johnson-Walker, Y;Landolfi, J;Roady, P;Rosser, M;Samuelson, J;Sander, W;Schnelle, A;Smith, B;Terio, K;Varga, C;Vieson, M2023-sp.csvVMNaNPathobiology III036005200390060002.89Vieson, MirandaNaNNaNAL1LECNaNNaNNaNNaN608NaN
200352023SPRING2023-spVM608Pathobiology IIIPathology, clinical pathology, and imaging of organ systems; epidemiology and food safety; and includes an integrative laboratory covering commonly encountered problems in infectious diseases. No graduate credit. 9 professional hours. Prerequisite: VM 607 and good standing in the veterinary professional curriculum, or consent of instructor.9 hours.No graduate credit. 9 professional hours. Prerequisite: VM 607 and good standing in the veterinary professional curriculum, or consent of instructor.NaNNaN55158AL1ABNaNNaNAOpen (Restricted)LectureLEC01:00 PM02:50 PMWNaNNaNBarger, A;Baumgartner, W;Colegrove-Calvey, K;Connolly, S;Delaney, M;Elston, C;Foreman, J;Hague, D;Hsiao, S;Johnson-Walker, Y;Landolfi, J;Roady, P;Rosser, M;Samuelson, J;Sander, W;Schnelle, A;Smith, B;Terio, K;Varga, C;Vieson, M2023-sp.csvVMNaNPathobiology III036005200390060002.89Vieson, MirandaNaNNaNAL1LECNaNNaNNaNNaN608NaN
200362023SPRING2023-spVM655Small Animal Medicine and Surgery IIITeaches the practice of Small Animal Medicine and Surgery in neurology, clinical toxicology, imaging, musculoskeletal disease, oncology, hematology, immune-related diseases, ophthalmology, and animal behavior. Surgery laboratories occur throughout this course and the companion course VM 656. No graduate credit. 7 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.7 hours.No graduate credit. 7 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.NaNNaN72913AL1AANaNNaNAOpenLaboratoryLAB08:00 AM11:50 AMTNaNNaNCappa, T;Elston, C;Foreman, J;Foss, K;Hague, D2023-sp.csvVMNaNSA Medicine and Surgery III033006800220060002.99Ridgway, Marcella DNaNNaNAL1LECNaNNaNNaNNaN655NaN
200372023SPRING2023-spVM655Small Animal Medicine and Surgery IIITeaches the practice of Small Animal Medicine and Surgery in neurology, clinical toxicology, imaging, musculoskeletal disease, oncology, hematology, immune-related diseases, ophthalmology, and animal behavior. Surgery laboratories occur throughout this course and the companion course VM 656. No graduate credit. 7 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.7 hours.No graduate credit. 7 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.NaNNaN72913AL1AANaNNaNAOpenLectureLEC08:00 AM10:50 AMFNaNNaNAldridge, R;Fan, T;Fick, M;Foreman, J;Foss, K;Gal, A;Garrett, L;Gleason, H;Hague, D;Haraschak, J;Keller, K;Labelle, A;Lundberg, A;McCoy, A;Moran, C;Ridgway, M;Sander, S;Selting, K;Welle, K;Wismer, T2023-sp.csvVMNaNSA Medicine and Surgery III033006800220060002.99Ridgway, Marcella DNaNNaNAL1LECNaNNaNNaNNaN655NaN
200382023SPRING2023-spVM655Small Animal Medicine and Surgery IIITeaches the practice of Small Animal Medicine and Surgery in neurology, clinical toxicology, imaging, musculoskeletal disease, oncology, hematology, immune-related diseases, ophthalmology, and animal behavior. Surgery laboratories occur throughout this course and the companion course VM 656. No graduate credit. 7 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.7 hours.No graduate credit. 7 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.NaNNaN72913AL1AANaNNaNAOpenLectureLEC09:00 AM10:50 AMMWNaNNaNAldridge, R;Fan, T;Fick, M;Foreman, J;Foss, K;Gal, A;Garrett, L;Gleason, H;Hague, D;Haraschak, J;Keller, K;Labelle, A;Lundberg, A;McCoy, A;Moran, C;Ridgway, M;Sander, S;Selting, K;Welle, K;Wismer, T2023-sp.csvVMNaNSA Medicine and Surgery III033006800220060002.99Ridgway, Marcella DNaNNaNAL1LECNaNNaNNaNNaN655NaN
200392023SPRING2023-spVM655Small Animal Medicine and Surgery IIITeaches the practice of Small Animal Medicine and Surgery in neurology, clinical toxicology, imaging, musculoskeletal disease, oncology, hematology, immune-related diseases, ophthalmology, and animal behavior. Surgery laboratories occur throughout this course and the companion course VM 656. No graduate credit. 7 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.7 hours.No graduate credit. 7 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.NaNNaN72913AL1AANaNNaNAOpenLectureLEC01:00 PM01:50 PMMTWRFNaNNaNAldridge, R;Fan, T;Fick, M;Foreman, J;Foss, K;Gal, A;Garrett, L;Gleason, H;Hague, D;Haraschak, J;Keller, K;Labelle, A;Lundberg, A;McCoy, A;Moran, C;Ridgway, M;Sander, S;Selting, K;Welle, K;Wismer, T2023-sp.csvVMNaNSA Medicine and Surgery III033006800220060002.99Ridgway, Marcella DNaNNaNAL1LECNaNNaNNaNNaN655NaN
200402023SPRING2023-spVM656Large Animal Medicine and Surgery IIITeaches the practice of Large Animal Medicine and Surgery in neurology, clinical toxicology, imaging, musculoskeletal disease, oncology, hematology, immune-related diseases, ophthalmology, and animal behavior. Surgery laboratories occur throughout this course and the companion course VM 655. No graduate credit. 3.5 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.3.5 hours.No graduate credit. 3.5 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.NaNNaN72914AL1AANaNNaNAOpenPackaged SectionPKG11:00 AM11:50 AMMWFNaNNaNAldridge, R;Austin, S;Foreman, J;French, D;Garrett, E;Gutierrez Nibeyro, S;Lowe, J;McCoy, A;Stewart, M;Wilkins, P2023-sp.csvVMNaNLA Medicine and Surgery III030005300430030002.85Garrett, Edgar FNaNNaNAL1LECNaNNaNNaNNaN656NaN
200412023SPRING2023-spVM656Large Animal Medicine and Surgery IIITeaches the practice of Large Animal Medicine and Surgery in neurology, clinical toxicology, imaging, musculoskeletal disease, oncology, hematology, immune-related diseases, ophthalmology, and animal behavior. Surgery laboratories occur throughout this course and the companion course VM 655. No graduate credit. 3.5 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.3.5 hours.No graduate credit. 3.5 professional hours. Prerequisite: VM 653 and VM 654 and good standing in the veterinary professional curriculum, or consent of instructor. Restricted to third year veterinary students only.NaNNaN72914AL1AANaNNaNAOpenPackaged SectionPKG02:00 PM02:50 PMMTWRFNaNNaNAldridge, R;Austin, S;Foreman, J;French, D;Garrett, E;Gutierrez Nibeyro, S;Lowe, J;McCoy, A;Stewart, M;Wilkins, P2023-sp.csvVMNaNLA Medicine and Surgery III030005300430030002.85Garrett, Edgar FNaNNaNAL1LECNaNNaNNaNNaN656NaN

Duplicate rows

Most frequently occurring

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92021FALL2021-faSOCW410Social Welfare Pol and SvcsExamination of social welfare within a historical context, addressing the economic, political, social and ideological influences that have shaped the social welfare system and programs. Critical study of the income maintenance system in the United States as a response to the problems of inequality of opportunity and income, poverty, and income security; consideration of alternative approaches with discussion of the social worker's role in the system. 3 undergraduate hours. 4 graduate hours.3 OR 4 hours.3 undergraduate hours. 4 graduate hours.NaNNaN58288BA1NaN4 hoursAUNKNOWNLecture-DiscussionLCD08:00 AM10:50 AMR20271010 W NevadaWegmann, K2021-fa.csvSOCWSocial Welfare Pol and Svcs7106101000000003.84Wegmann, Kate MBONL410NaN8
102021FALL2021-faSOCW416Child Welfare Issues &amp; TrendsThis course examines theoretical and programmatic aspects for child welfare practice. Emphasis is placed on the roles and functions of child welfare workers, including engagement, assessment, intervention and permanency planning. 4 undergraduate hours. 4 graduate hours. Prerequisite: SOCW major. For majors only.4 hours.4 undergraduate hours. 4 graduate hours. Prerequisite: SOCW major. For majors only.NaNNaN63134AA1NaN4 hoursAUNKNOWNLecture-DiscussionLCD01:00 PM03:50 PMR20181010 W NevadaLindsey, B2021-fa.csvSOCWChild Welfare Issues & Trends1212100020010103.57Lindsey, Brenda CAONL416NaN8
32020SPRING2020-spSOCW500SW Practice with Indiv and FamSystematically and critically examines the theory, procedures, and techniques of selected practice models within four main approaches to social work: cognitive-behavioral, systemic (family and ecological systems; crisis intervention), task-centered, and radical-structural (structural; feminist). Uses selected criteria to analyze and assess those models, examines outcome research, and identifies current practice issues. Prerequisite: SOCW 400.4 hours.Prerequisite: SOCW 400.NaNNaN32838AA1NaNNaNAUNKNOWNLecture-DiscussionLCD09:00 AM12:55 PMSNaNNaNHarden, K2020-sp.csvSOCWSW Practice with Indiv and Fam0163310010100003.65Harden, Kimberly LNaNLCD500NaN4
42020SPRING2020-spSOCW507School Social Work PracticeExamination of the design and delivery of school social work interventions with special emphasis given to students with physical/mental disabilities and vulnerable populations. Course content provides a foundation for the development of a comprehensive and in-depth understanding of an ecological systems approach to social work practice based upon a foundation of professional values and ethics. Prerequisite: SOCW 400.4 hours.Prerequisite: SOCW 400.NaNNaN3284418AA1NaNNaNAUNKNOWNLecture-DiscussionLCD09:00 AM12:00 PMS20181010 W NevadaLindsey, B2020-sp.csvSOCWSchool Social Work Practice0153110000010003.73Lindsey, Brenda CNaNONL507NaN4
52020SPRING2020-spSOCW509Adv Clin Assess &amp; InterviewingAdvanced practice class designed to enhance students' understanding of clinical assessment and interviewing methods. Includes methods for therapeutically intervening with clients who are highly distressed, angry or agitated, resistant or involuntarily mandated for treatment, experiencing severe symptoms, or who have unique and complex problems. Clinical interviewing skills taught in this class will build upon knowledge and skills acquired in previous direct practice classes. Prerequisite: SOCW 400 and SOCW 552.4 hours.Prerequisite: SOCW 400 and SOCW 552.NaNNaN5320319DA1NaNNaNAUNKNOWNLecture-DiscussionLCD09:00 AM12:30 PMSARRIllini CenterCintron, V2020-sp.csvSOCWAdv Clin Assess & Interviewing1461010000000003.94Cintron, ValerieNaNONL509NaN4
62020SPRING2020-spSOCW509Adv Clin Assess &amp; InterviewingAdvanced practice class designed to enhance students' understanding of clinical assessment and interviewing methods. Includes methods for therapeutically intervening with clients who are highly distressed, angry or agitated, resistant or involuntarily mandated for treatment, experiencing severe symptoms, or who have unique and complex problems. Clinical interviewing skills taught in this class will build upon knowledge and skills acquired in previous direct practice classes. Prerequisite: SOCW 400 and SOCW 552.4 hours.Prerequisite: SOCW 400 and SOCW 552.NaNNaN5940419AA1NaNNaNAUNKNOWNLecture-DiscussionLCD08:30 AM12:00 PMS20231010 W NevadaCintron, V2020-sp.csvSOCWAdv Clin Assess & Interviewing1451100000010003.82Cintron, ValerieNaNONL509NaN4
72020SPRING2020-spSOCW509Adv Clin Assess &amp; InterviewingAdvanced practice class designed to enhance students' understanding of clinical assessment and interviewing methods. Includes methods for therapeutically intervening with clients who are highly distressed, angry or agitated, resistant or involuntarily mandated for treatment, experiencing severe symptoms, or who have unique and complex problems. Clinical interviewing skills taught in this class will build upon knowledge and skills acquired in previous direct practice classes. Prerequisite: SOCW 400 and SOCW 552.4 hours.Prerequisite: SOCW 400 and SOCW 552.NaNNaN6545319BA1NaNNaNAUNKNOWNLecture-DiscussionLCD01:00 PM04:30 PMS20181010 W NevadaAviram, A2020-sp.csvSOCWAdv Clin Assess & Interviewing1163100000000003.92Aviram, Amy SNaNONL509NaN4
82020SPRING2020-spSOCW519Public School Policy/ServicesPresents content on children with physical and mental disabilities, educational policies related to vulnerable populations, and federal and state legislation, with particular emphasis given to the Individuals with Disabilities Act (IDEA). The following topics are highlighted: eligibility requirements, general characteristics of the disabling conditions, education as a continuum from early childhood to adulthood, school finance, and current educational issues. Content is presented pertaining to meeting the needs of exceptional children, students with other special needs, and their families in public schools and the community. Prerequisite: SOCW 410.4 hours.Prerequisite: SOCW 410.NaNNaN3283418AA1NaNNaNAUNKNOWNLecture-DiscussionLCD01:00 PM03:00 PMS20181010 W NevadaWilson Smith, C2020-sp.csvSOCWPublic School Policy/Services0180210000000003.89Wilson Smith, Carol JNaNONL519NaN4
112021FALL2021-faSOCW503Trauma Informed Social Work with Children and AdolescentsThis course uses a case study and inquiry based approach to foster student learning of the core concepts of trauma (theory and foundational knowledge) and evidence-based practice interventions effective in treating children, youth, and families that experience trauma. Cases discussed include children, youth, and families exposed to traumatic events (i.e. abuse, neglect, domestic violence, community violence and natural disasters). Strength-based practice interventions that build on existing child and family strengths that enhance growth and resiliency after trauma are studied. Prerequisite: SOCW 400.4 hours.Prerequisite: SOCW 400.NaNRestricted to students enrolled in the MSW program.62085AA1NaNNaNAUNKNOWNOnlineONL09:00 AM04:00 PMSNaNNaNLeytham Powell, T2021-fa.csvSOCWTrauma Informed SW w/ Children0230000010000003.92Leytham Powell, TaraAONL503NaN4
122021SPRING2021-spSOCW500SW Practice with Indiv and FamSystematically and critically examines the theory, procedures, and techniques of selected practice models within four main approaches to social work: cognitive-behavioral, systemic (family and ecological systems; crisis intervention), task-centered, and radical-structural (structural; feminist). Uses selected criteria to analyze and assess those models, examines outcome research, and identifies current practice issues. Prerequisite: SOCW 400.4 hours.Prerequisite: SOCW 400.NaNNaN32838AA1NaNNaNAOpen (Restricted)Online LectureOLC09:00 AM12:00 PMSNaNNaNHarden, K2021-sp.csvSOCWSW Practice with Indiv and Fam0151320000000003.79Harden, Kimberly LNaNOLC500A4